affiliation in research paper meaning

• Interlibrary Loan and Scan & Deliver
• Course Reserves
• Purchase Request
• Collection Development & Maintenance
• Current Negotiations
• Ask a Librarian
• Instructor Support
• Library How-To
• Research Guides
• Research Support
• Study Rooms
• Research Rooms
• Partner Spaces
• Loanable Equipment
• Print, Scan, Copy
• 3D Printers
• Poster Printing
• OSULP Leadership
• Strategic Plan

Scholarly Articles: How can I tell?

• Journal Information
• Literature Review

Author and affiliation

Learn more about the author.

• Introduction
• Specialized Vocabulary
• Methodology
• Research sponsors
• Peer-review

If you can't find an author affiliation or want to learn more about the authors and their credentials, here are some ways to do so:

• Search for the author on Google. Sometimes you can find a personal page about an individual. Many of the faculty members at OSU have a website that lists their credentials (education) and research.
• Do a search in one of the online databases to see what else the author has written. Is this person someone who published a lot in this field? For example, a search in the Academic Search Complete database for the author Sandra Hofferth shows the articles she has co-authored on a range of children's issues .
• Look up the institution. What kind of institution is it?  Is the author still affiliated with the institution?

One of the first things to look for is the author or authors. In a research article, the authors will list their affiliation, usually with a university or research institution. In this example, the author's affiliation is clearly shown on the first page of the article. In a research article, you will never have an anonymous author or need to look for the author's name or affiliation.

• << Previous: Literature Review
• Next: Abstract >>
• Last Updated: Apr 15, 2024 3:26 PM
• URL: https://guides.library.oregonstate.edu/ScholarlyArticle

Contact Info

121 The Valley Library Corvallis OR 97331–4501

Phone: 541-737-3331

Services for Persons with Disabilities

In the Valley Library

• Oregon State University Press
• Special Collections and Archives Research Center
• Undergrad Research & Writing Studio
• Graduate Student Commons
• Tutoring Services
• Northwest Art Collection

Digital Projects

• Oregon Explorer
• Oregon Digital
• ScholarsArchive@OSU
• Digital Publishing Initiatives
• Atlas of the Pacific Northwest
• Marilyn Potts Guin Library  
• Cascades Campus Library
• McDowell Library of Vet Medicine

We use cookies on this site to enhance your experience

By clicking any link on this page you are giving your consent for us to set cookies.

A link to reset your password has been sent to your email.

Back to login

We need additional information from you. Please complete your profile first before placing your order.

Thank you. payment completed., you will receive an email from us to confirm your registration, please click the link in the email to activate your account., there was error during payment, orcid profile found in public registry, download history, understanding author affiliation and accurately mentioning it in different scenarios.

• Charlesworth Author Services
• 16 April, 2022

In academic publishing, the affiliation of an author is the place (institution) at which the author conducted the research that they have reported / written about . However, given the frequent mobility of academics, that place may not necessarily be the place the author happens to be based at the time of submitting the paper . This article explains the significance of affiliation and illustrates how to accurately mention your affiliation in different scenarios.

The importance of affiliation

In some cases, affiliation is linked to authenticity . Imagine a research paper on field pollination of rice by an author whose affiliation is that of an institute in the polar region. It is not that this work cannot be done, but it would seem incongruous and may raise doubts.

In many cases, it is a matter of prestige . Science may be democratic, but not all research institutions and laboratories are considered equal.

Some may be better equipped than others. Some may have more luminaries on their staff – people who have outstanding work (or even prizes) to their credit. Some may have enviable collections of records or research material. 

Therefore, by proxy, work carried out at those institutions is regarded more highly, at least initially, than that carried out at lesser-known institutions.

A study by Peters and Ceci (1982) found that when 12 already published papers were resubmitted after doctoring the affiliations to replace the original high-status institutions with fictitious ones with no status in the field, eight of those papers were rejected.

Mentioning your affiliation in a paper

In nearly all published papers, affiliations of their authors are given after their names but before the abstract. The typical sequence is: 

• Title of the paper
• Names of authors
• Affiliations
• Abstract and keywords

Paper with title, author names, affiliation, abstract and keywords

Mentioning affiliation and address

Authors of research papers must keep an important distinction in mind: that an affiliation is not the same thing as a mailing address . The former names the institution at which the work in question was carried out whereas the latter simply supplies the current contact details of the author. 

For example…

A PhD candidate submitting a paper based on their doctoral work should name, as their affiliation, the university/institution that is granting them the doctorate. However, that author may have since moved to another institution for a post-doctoral job. This is not considered their affiliation, but just provides their current contact details.

Therefore, you may have to name two institutions in your manuscript: 

• Under Affiliation : Name the institution where the work (that forms the subject of the present study) was undertaken.
• Under Current address : Name the institution at which you happen to be working at the time of submission or even your home address if you have retired. 

Note : The ‘current address’ serves as the means of contact and can change; the affiliation cannot. 

Mentioning affiliation when you change your institute

It may also happen that when you submitted the paper, you were stationed at Institute A and accordingly gave that as your contact address, and subsequently, you moved to Institute B. In such cases, so long as your paper is yet to be published, you should inform the journal of your new current address at Institute B. The paper is based on the work you carried out while you were based at institute A, which constitutes the affiliation and remains unchanged.

Mentioning affiliations for multi-author papers

Most research papers have multiple authors and not all of them may have the same affiliation. To match their names to their affiliations, journals may use the method used for indicating footnotes . The names of authors are followed by superscript letters, numerals or other symbols, and the same symbols precede the respective affiliations.

We recommend : Note the journal’s preferred method (letters, numerals or other symbols) and be sure to  follow the  journal guidelines  when  preparing your manuscripts for submission .

Numerals indicating authors (above) and their affiliations (below) in a paper

Dealing with affiliations during peer review

To avoid the kind of bias mentioned earlier, affiliation information is removed in manuscripts sent out for review: in a blind review , the reviewers do not know who wrote the paper under review, nor their institutional affiliation. To make this easier, many journals ask that such identifying information be separated from the body of the paper . Authors are advised to attend to the journal’s instructions in this regard, which typically involve a separate title page explicitly showing the names and affiliations. This page is usually removed before sending the paper to reviewers.

Maximise your publication success with Charlesworth Author Services.

Charlesworth Author Services, a trusted brand supporting the world’s leading academic publishers, institutions and authors since 1928.

To know more about our services, visit: Our Services

Share with your colleagues

Related articles.

Authorship of academic papers

Charlesworth Author Services 11/07/2017 00:00:00

Understanding and following the Information for Authors (Author Guidelines)

Charlesworth Author Services 12/01/2022 00:00:00

What to pay attention to when submitting your article

Charlesworth Author Services 16/03/2021 00:00:00

Related webinars

Bitesize Webinar: How to write and structure your academic article for publication - Module 1: Know when are you ready to write

Charlesworth Author Services 04/03/2021 00:00:00

Bitesize Webinar: How to write and structure your academic article for publication: Module 4: Prepare to write your academic paper

Bitesize Webinar: How to write and structure your academic article for publication: Module 6: Choose great titles and write strong abstracts

Charlesworth Author Services 05/03/2021 00:00:00

Bitesize Webinar: How to write and structure your academic article for publication: Module 11: Know when your article is ready for submission

How to use Footnotes and Endnotes in academic papers

Charlesworth Author Services 28/01/2022 00:00:00

From writing to submission: Checklist for writing and submitting a Quality Scientific Article

Charlesworth Author Services 10/11/2021 00:00:00

Differences between Single-Blind and Double-Blind Peer Review

Charlesworth Author Services 22/09/2020 00:00:00

What Is Affiliation in Research Paper?

H ave you ever heard of the term “affiliation” in a research paper? Do you know what it is and why it is important? Or, in other words, is “affiliation” important?

In today’s article, we examine every angle of Affiliation in research papers and discuss its importance and how Affiliation is listed and written.

If “What is affiliation in research paper?” is one of your concerns, stay with us to learn more about the other important element in conducting research: Affiliation.

Table of Contents

What Is the Meaning of Affiliation in a Research Paper?

In the research world, an affiliation is associated with a research institution , representing the organizational framework under which an author conducts their research.

It is crucial for scientific papers, particularly those published in Scopus/Web of Science-indexed journals. Affiliation serves as a means of identifying the institutional support and expertise that contributed to the research findings.

Where can you find the Affiliation in a research paper?

Author affiliation is usually listed in the research papers after the author’s name. Accurately presenting affiliations is essential for maintaining transparency and upholding the credibility of scientific publications . If you are looking for further importance of the Affiliation, look at the section below.

Why Are Affiliations Important in Research Publications?

Including your employer’s name indicates who controls research integrity since these institutions usually have review committees that authorize research, and it is the importance of Affiliation.

Due to the increase in collaborations among international authors, each author may have several affiliations in their articles.

Do you know what the role of Affiliation in research makes it essential?

In research papers, affiliations serve multiple purposes, such as:

1.    Identifying Institutional Funding

As mentioned above, affiliations indicate the institutions that funded and supported the research and allow readers to assess the resources and expertise that contributed to the findings.

2.    Presenting Authorizations

Demonstrating the author’s connection to the institutions by Affiliations lends credibility to their work and expertise.

3.    Enhancing Transparency

Accurate list affiliations will promote transparency and accountability in research and let the readers know the study’s methodology and findings.

After all this information, let’s find out how an author can list the Affiliation in a research paper.

How Do You List Your Affiliation in A Research Paper?

In the listing process, you must maintain some key points and follow the same rule. Although the method might differ in some journals, follow the journal’s preferred method (such as using the correct letters, numerals, or other symbols) and stick to the journal guidelines while listing the Affiliation.

Follow the steps below to write an affiliation on your research paper correctly:

• If you have worked in a school or university:
• Authors Name(S)
• Department or School Name
• University Name
• State, City, Country
• Email Address
• If you have worked in an institute or a company:
• Authors name(s)
• Research institute or company name
• State, city, country
• Email address
• If you have worked as an independent author:
• Freelance research or independent scholar

Here is the primary method that you can use to list your Affiliation in a research paper.

What Are the Consequences of Inaccurate Affiliation Reporting?

Publishing research under an inaccurate or false affiliation can be considered academic misconduct. This practice is misleading because it can misattribute funding responsibility or research ethics to the wrong institution.

It is important to note that publishing research conducted at one institution while currently affiliated with another is generally acceptable.

In this case, the author typically lists their Affiliation as the institution where the research was conducted, sometimes accompanied by their current Affiliation.

Sometimes, the affiliations may be left out due to their length or complexity. But do not be worried!

If the leading institution (where the experiments were conducted) is correctly listed, omitting other less relevant affiliations is acceptable.

Read More: LaTeX Code for Research Paper

In this part, you became familiar with the crucial role of affiliations in research papers, providing context for the author’s work and establishing credibility.

We also mentioned how to accurately list affiliations, which is essential for maintaining transparency and authenticity of your academic and scientific publications.

We hope the “What Is Affiliation in Research Paper” article has been informative. We want you to share your thoughts and insights on the importance of affiliations in research papers by commenting below.

LaTeX Code for Research Paper

How to write equation in latex, related articles.

How To Mention Corresponding Author in Paper?

What Is Article Number in Journal?

Independent researcher affiliation

Is 5 A Good Impact Factor in a Journal?

Leave a reply cancel reply.

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

In this section:

• Advertising and sponsorship
• Authorship and contributorship
• Competing interests
• Copyright and authors’ rights
• Correction and retraction policies
• Data protection policy
• Data sharing
• Editor Roles & Responsibilities
• Gender identity and race & ethnicity data in ScholarOne
• Patient and public partnership
• Patient consent and confidentiality
• Peer Review Terms and Conditions
• Publication embargo
• Reproducing third party illustrative materials
• Research Ethics
• Rapid responses
• Scientific misconduct
• Trial registration

This policy ensures that contributors who have made substantive intellectual contributions to an article are given credit and that contributors understand their role in taking responsibility and being accountable for what is published. Contributors are either author contributors (meaning that they meet all four authorship criteria – see below) or non-author contributors.

BMJ credits and lists contributors in two ways:

• Authorship – we publish a list of authors’ names at the beginning of the paper in the byline
• Contributorship – we publish a contributorship statement at the end of the paper, giving details of who did what in planning, conducting, and reporting the work. This should include all author contributors and may include non-author contributors.

We also publish an acknowledgements statement at the end of the paper, detailing those who helped in carrying out the research but that have not been recognised as contributors, and for personal expressions of gratitude.

Submitting author

Corresponding author, joint first authorship, collaborators (group authorship), deceased authors, alteration to authorship, acknowledgements.

The International Committee of Medical Journal Editors Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals ( ICMJE Recommendations 2019 ) recommend that authorship be based on the following four criteria:

• Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND
• Drafting the work or revising it critically for important intellectual content; AND
• Final approval of the version to be published; AND
• Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

BMJ requires that all those designated as authors should meet all four ICMJE criteria for authorship, and all who meet the four criteria should be identified as author contributors. We recognise only natural persons (an individual human being, as opposed to a private or public organisation) as authors. These authorship criteria are intended to reserve the status of authorship for those who deserve credit and can take responsibility for the work. The criteria should not be used to disqualify colleagues from authorship who otherwise meet authorship criteria by denying them the opportunity to meet criterion number 2 or 3. Therefore, all individuals who meet the first criterion should have the opportunity to participate in the review, drafting and final approval of the manuscript.

Contributors who have contributed materially to the paper but whose contributions do not justify authorship should be described clearly in the contributorship statement .

In addition to being accountable for the parts of the work they have done, an author should be able to identify which co-authors are responsible for specific other parts of the work. In addition, authors should have confidence in the integrity of the contributions of their co-authors.

Submitting authors should provide assurance that all authors included on a paper fulfil the criteria of authorship. We also ask for assurance that there is no one else who fulfils the criteria that has been excluded as an author.

When we encounter disagreements among authors we follow guidance from the Committee on Publication Ethics (COPE).

The submitting author takes primary responsibility for submitting the article to the journal using our manuscript submission system ScholarOne and for communicating with the journal during the article submission, peer review and revision process. They ensure that all of the journal’s administrative requirements are properly completed. These include, but are not limited to, providing details of authorship, ethics committee approval, clinical trial registration documentation, and gathering conflict of interest forms and statements. These tasks may be delegated to one or more co-authors, but the submitting author remains responsible for them.

When you submit your article through our submission system you will be asked to provide a name, email address and institutional affiliation for all author contributors. In the final published article author names, institutions and addresses will be taken from these completed fields and not from the submitted Word document.

Affiliations listed should be those where the work was carried out at the time the research/article was written. If institution details appear incorrectly these can be directly amended under ‘Actions’ by selecting the ‘Edit’ drop down next to each author.

All author contributors receive a confirmation email when an article has been submitted and when a final decision is made.

The submitting author should assign the corresponding author when providing author details (see below for more information about the corresponding author role). The submitting author and corresponding author can be the same person.

The corresponding author, as listed on ScholarOne, takes primary responsibility for completing all necessary actions after acceptance of the manuscript and communicating with the journal and with readers after publication. All email communication from BMJ will be sent to the corresponding author including:

• The timeline for your article proof with a link to Publishing at Work where you can track your article’s status
• If your article will be published open access or in colour in the print edition of the journal, you will receive an email from Rightslink with payment options and instructions. If you are not making the payment yourself, you may forward the email to the person or organisation that will be paying on your behalf
• A link to review and approve the proof when available
• Confirmation that your article has been published online
• Notifications when a response has been posted to your article

Find out more about what to expect when your article has been accepted .

Although we include only one corresponding author on ScholarOne for email communication, multiple authors can be listed with correspondence information in the author byline of the final published article. This information can be included at the article proof stage, after acceptance.

Note, the policy for The BMJ differs and can be found here

Joint first authors can be indicated by the inclusion of the statement ‘X and X contributed equally to this paper’ in the contributorship statement.

Collaborators are a large group of multi-author contributors (e.g. a specific consortium, committee, study group or the like). Collaborators should decide who will be an author before the work is started and confirm who is an author before submitting the manuscript for publication. All members of the group named as authors should meet all four criteria for authorship as detailed above. They will also be expected as individuals to complete conflict-of-interest disclosure forms and provide a summary in the relevant section.

The collaborator group name(s) should be included in the main author list on ScholarOne. The collaborator group name(s) followed by the individual names should also be listed in the ‘Collaborator’ field on ScholarOne. BMJ will list the author group name(s) in the author byline, with the full list of individual names included in a collaborator statement at the end of the article. Details of the group’s contributions should also be listed in the ‘Contributorship statement’ field on ScholarOne.

If the journal is indexed in PubMed (MEDLINE and/or PubMed Central), the group name will be listed in the author byline and the names of individual group members entered as collaborators on the PubMed record to ensure individual due credit.

The BMJ Effect of a collector bag for measurement of postpartum blood loss after vaginal delivery: cluster randomised trial in 13 European countries

PubMed record >>

BMJ Open Establishing a core outcome set for treatment of uncomplicated appendicitis in children: study protocol for an international Delphi survey

AI technologies will not be accepted as an author(s) of any content submitted to BMJ for publication. BMJ only recognises humans as being capable of authorship since they must be accountable for the work.

Deceased persons deemed appropriate as authors should be highlighted to the Editorial Assistant when submitting your article and should also be included in your contributorship statement.

If an author’s affiliation has changed during the course of the work, the author may either list the affiliation at the time that the research (or most significant portion of the research) was conducted, or their current affiliation, or both. The change of affiliation can be explained in an acknowledgements section.

Any change in authors after initial submission and before publication must be approved by all authors. This applies to additions, deletions, a change of order to the authors’ names or a change to the attribution of contributions. Any alterations must be explained to the Editor. The Editor may contact any of the authors and/or contributors to ascertain whether they have agreed to any alteration.

Contributorship statement

A contributorship statement is required for every article submitted and should outline who has contributed what to the planning, conduct and reporting of the work described in the article. A contributorship statement should include author contributors, non author contributors and group author contributors (collaborators). Contributors who have contributed materially to the paper but whose contributions do not justify authorship should be described clearly in the contributorship statement; for example, “served as scientific advisors”, “critically reviewed the study proposal”, “collected data” or “provided and cared for study patients”.

Researchers must determine among themselves the precise nature of each person’s contribution, and we encourage open discussion among all participants to reach a consensus.

This is also the appropriate place to include contributions by patients or members of the public who have assisted as research volunteers, giving their names and specific roles. We encourage authors to fully acknowledge the contribution of patients and the public to their research where appropriate.

All individuals named in the contributorship statement must give permission to be included, as readers may infer their endorsement of the data and conclusions of the paper. It is the responsibility of the submitting author to ensure that permission is obtained and to be able to provide evidence of this if required.

Each contributorship statement must make clear who is responsible for the overall content as guarantor. The guarantor accepts full responsibility for the finished work and/or the conduct of the study, had access to the data, and controlled the decision to publish.

• To ensure transparent declaration of AI, authors should:

2. Transparent declaration includes a description of: 

• What AI technology was used (the name of the technology)
• Why this AI technology was used (the reason for its use)
• How the AI technology was used (what the task of the technology was)
• Consider including a summary of the input, output, and the way in which the AI output was reviewed on the part of the authors as supplementary files or additional information for the editor to review. The editor may ask for more information and/or for information to be added to the content for internal use and/or for publication.

An acknowledgements statement may be included at the end of the paper, detailing those who helped in carrying out the research but who have not been recognised as contributors, as well as for personal expressions of gratitude.

Because acknowledgment may imply endorsement by acknowledged individuals of a study’s data and conclusions, authors are strongly advised to obtain permission to be acknowledged from all acknowledged individuals before submitting to any BMJ journal.

A uthor name change requests

As an inclusive publisher, BMJ wishes to ensure a smooth process and experience to facilitate author name changes after publication. For more information on how to request an author name change in an existing publication see our corrections policies.

Last updated: March 2023

Affiliation searches: the Why, What, and How of our Canonical Affiliation Feature

Carolyn stern grant and matthew templeton (ads), 15 jan 2020.

One of the features new to ADS since the new interface has launched is search and organization by institutional affiliation. The 15 million publications in ADS have more than 35 million combined author affiliations. The ADS has long wanted to have these data in a searchable format, and we introduced a new curated affiliation feature in early 2019. The project involved matching existing publisher-provided affiliation strings to unique, curated affiliation identifiers and institution strings, stored in an internal affiliation database, and constructing a pipeline to match the publisher-provided affiliation strings in incoming new publications to the appropriate entry in this database. Constructing the internal affiliation database took a lot of manual labor – in fact, much more than the pipeline that resolves the affiliation strings – and while this database will never be 100 percent complete, we’re hoping to use machine learning techniques to reduce the amount of human effort required to match affiliation data to their identifiers.

Users are reminded that while affiliation information is largely complete for recent refereed literature, not all records contain an affiliation, therefore, searching by affiliation alone will inherently be incomplete. We strongly recommend combining affiliation searches with author searches for best results.

Using affiliations as part of your search strategy: Author searches and the Affiliation facet

Affiliation data for papers are available via the “Affiliations” facet in ADS. As an example, if you search for a first author, you’ll get a list of papers having that first author, and on the left-hand side of the results page you’ll see ways to refine those results – who their coauthors are, whether the paper is in the astronomy, physics, or general database, whether it’s a refereed publication or not, and so on. One of those facets is “Affiliations”, and by clicking it you’ll get a list of institutional affiliations sorted by the number of papers having that affiliation.

Using one of this blog’s coauthors, searching for first_author:“Templeton, Matthew” brings up a list of his papers along with a few other “Matthew Templeton”s who are active researchers in ADS-relevant fields. Selecting the “astronomy” collection and “limit to” yields (mostly) just his set of first-authored publications, and refereed lowers it further to a manageable number. If you then open the Affiliations facet, you’re presented with a list of all affiliations (of any author) contained within his papers. Matthew Templeton’s historical affiliations – AAVSO, New Mexico State University, Yale University, Los Alamos National Laboratory – are among the most common in the list. However, you also find affiliations of co-authors too, for example Iowa State and the Center for Astrophysics.

Using affiliations as part of your search strategy: Direct affiliation searches

Affiliations in the ADS have been indexed in several different fields, with the intention of allowing multiple use cases. We have currently assigned identifiers with parent/child relationships, such as an academic department within a university. A child may have multiple parents, but we restrict a child from having children of its own. This has required a few modifications to remain useful. For instance, so that University of California schools can identify departments, we have assigned them a parent status, even though the “University of California System” should really be the parent level. Likewise, NASA’s Goddard Space Flight Center is at a parent level, as are France’s CNRS institutions to allow for further subdivision. Further work on a schema to allow more complex relationships between institutions is under development in conjunction with work by the ROR Community .

We’ve recently changed the way we index affiliations by introducing a new search field: “affil”. Affil combines all of the available affiliation data – raw strings, canonical strings, IDs, and abbreviations into a single, searchable field. It’s intended to be a more comprehensive search of both raw strings and the enriched institution asssignments we’ve applied to our data. So for example, searching for affil:“UCB” will return papers where ‘UCB’ matches some part of the raw affiliation, but also return affiliations we’ve matched to the University of California at Berkeley. Because “UCB” is used by some authors to abbreviate other universities (for example “Catholic University of Brasilia, Brazil” or “University of Colorado, Boulder”), the affil search also finds these, but they can be de-selected in the affiliation facet.

In addition to this new field, we’ve maintained the original search terms for affiliations that we deployed last year:

aff: raw affiliation string, searchable word-by-word

aff_id: a string containing one or more of the affiliation IDs listed in our mapping of organizations to identifiers . This field will soon also accept 9-digit ROR IDs.

inst: the abbreviated institution name (e.g. “U Adelaide”) listed in our mapping of organizations to identifers .

So, you could search for aff:Harvard and get back all affiliations that contain Harvard in the affiliation string – including “Harvard Street”. To ensure you get only the University, you could search for aff:“Harvard University” but that would return only affiliation strings with that exact phrase, excluding “Harvard Univ.”, “Harvard U”, etc. Better would be to use the identifier for Harvard University by searching aff_id:A00211 . This returns all affiliations which contain Harvard University at the parent level. Best yet would be to search by institution, inst:“Harvard U” which returns affiliations which contain Harvard University at the parent level, plus all affiliations for all of Harvard University’s children.

How we got here: the curation and pipeline process

The human element: curation

Production of the initial affiliation database began with two human-generated projects: creating a set of institutional identifiers, and matching affiliation metadata to these identifiers as precisely as possible. Both of these were done almost entirely by ADS Lead Curator Carolyn Grant.

There are hundreds of thousands of organizations around the world, and in principle any institution or business can be assigned an identifier. The overwhelming majority of these are not relevant to ADS, so the first task was to establish a list of institutions that match affiliations in our metadata. The current list stands at about 6600 departments, organizations, and parent institutions in all geographic regions of the world. Many of these institutions are also tagged with a second identifier, namely the identifier of the parent institution. For example, the parent institution of the Department of Physics at the University of California at Irvine is “University of California at Irvine”. For now, our affiliations have two levels only, so in this example, we do not assign “The University of California System” as the parent of UC Irvine, but our list of identifiers includes System identifiers in cases of ambiguity (e.g. a publisher-provided affiliation “Univ. of Cal.” with no city or other unambiguous identifying information).

The second, and by far most time-consuming, part of this process was the initial classification of millions of individual affiliation strings from the ADS bibliographic database. This involved both extraction of the author affiliation strings from the database and identification of the institution represented by that string.

As an example, a typical string might be “Physics Dept., UC Irvine” which would correspond to the affiliation of “Department of Physics, University of California at Irvine” and then assigning this string its corresponding alpha-numeric ID. The process sounds straightforward, but extraction of all strings relating to “Department”, “Physics”, “University of California”, and “Irvine” can be long and tedious, especially if you’re searching thousands of possible strings. There are ways to speed that process (e.g. by searching for “Irvine” and “Physics” in all strings, and then looking through just that set of results), but at the beginning it was an entirely human-curated process. Complicating things is the fact that many authors are affiliated with more than one institution, for example “Astronomy Dept., U. Texas – Austin, and UNAM-Morelia”. Publishers – especially smaller publications and conference proceedings – rarely use standardized text for affiliations, and even more rarely list multiple affiliations in separate entities in their metadata. So the curation process also involves splitting those multiple affiliations and then characterizing each affiliation separately. That process was repeated over and over for the 6600 affiliations that each occasionally had thousands or tens of thousands of different non-unique publisher-provided strings.

Cleaning the data turned out to be more of an art than a science. There exist curation tools for cleaning up messy data – the one we used almost exclusively was Open Refine (called Google Refine at the time). This allowed bulk substitutions, expansions, translations and more. But cleaning is not enough. For example, changing all instances of “UC Irvine” to “University of California Irvine” may seem like a good idea, except that new instances of “UC Irvine” are likely to continue coming in.

Automated Pipelines: assigning identifiers to specific author-affiliation-bibcode entities

With the initial set of affiliation strings identified we, along with ADS backend programmer Stephen McDonald, designed an automated pipeline to add canonical affiliation identifiers to our database of 15 million references. It’s a simple process. First we normalize all of the strings with all uppercase letters, remove spaces and a subset of punctuation marks not needed for disambiguation, which results in a reduced number of matching strings; often, strings will be identical without punctuation, such as strings with “… U.S.A.” and “… USA”. We then assign the strings to a dictionary, where the strings themselves are the keys, and the identifiers are the values. If a normalized incoming string matches a key in the dictionary, we assign it the appropriate aff_id, and the record with augmented affiliation data is sent back to our database of metadata for use in the next reindexing process.

We’ve entirely automated the process, so once the dictionary of affiliation strings and matched identifiers is created, it’s a hands-off component of our metadata processing pipeline. We update dictionaries about once a month, and occasionally more often if we have a large batch of incoming unmatched affiliation strings. This process is similar to the first curation step, with the only difference being that we prioritize strings according to how many times they appear. For example, if we receive a number of papers from collaborations with hundreds or thousands of authors and their affiliations don’t exactly match our dictionary, we need to add them. Early on in this process, we came across strings that had many thousands (and sometimes tens of thousands) of occurrences, but the latest batch of unclassified strings appear fewer than 550 times in our entire database. This maximum frequency drops each time we create a new dictionary.

However, we’re getting close to the limit of what we can do with human classification without lots of effort. Our most recent pass through our metadata has just over 4.8 million unique unclassified strings yielding a total of 16.6 million unclassified affiliations. There are about 6000 unclassified affiliations that appear 100 times or more in the metadata and assigning them aff_ids would provide a lot of new information. However, over 65 percent of unclassified affiliations occur fewer than 10 times, and 13 percent of unmatched affiliation strings only appear once in all of our affiliation metadata .

This is too much to work through by hand, but it represents valid affiliation data for millions of author-affiliation pairs. For the unmatched affiliations that occur fewer than 100 times, we’re using machine learning techniques to try and assign IDs. For now, we’re using scikit-learn tools to try and match unknown strings to IDs. Specifically, we’re using scikit-learn’s feature_extraction tools for analyzing the known affiliation strings, and the SGDClassifier to generate models. We then pass the unknown strings through the model and extract both a best match and a confidence estimate. The process can be very memory intensive because of the large number of classes – nearly 6600, one for each aff_id. A typical data set of 25,000 unmatched strings takes about an hour on a modest workstation (Apple iMac with an Intel i5 processor and 24 GB of memory).

We’ve found the process is reliable to the limits of the input dictionary, and also found that it’s much more of a data curation problem than a machine learning problem. Curation of a machine learning model isn’t trivial. It’s very sensitive to both errors in assigned IDs (which are rare but do occasionally occur) and to ambiguous affiliation strings or strings that can’t be split cleanly. Examples of the latter include affiliation strings that include (say) both a national laboratory affiliation and a university one (e.g. the Italian INFN centers, NIKHEF member institutions, and US DOE Laboratories), or cases where we have an incomplete mapping of all parent-child relations (e.g. where we have an ID for a university’s Department of Physics, but not a Department of Statistics or Materials Science).

For now, we’re using machine learning to assist with curation, but we’re not yet confident enough to pass it an affiliation string and guarantee it returns the correct ID; it’s not (yet) a part of our hands-off pipeline. It’s an ongoing process of improvement, and always will be as new metadata keeps coming in.

What’s next?

Searching ADS by affiliation is already very useful for helping with disambiguation, and for helping build institutional publication lists. We hope to make it even more powerful by integrating our system with ROR and integrating publisher-specific identifiers in our workflow. We are actively working with other projects to extend ROR identifiers (which are assigned one per institution) to the department level. In addition, we hope to improve the user experience by coupling affiliation with authors, adding hover-over expansion of abbreviations, and implementing auto-complete with the institution search. As always, we welcome feedback and corrections.

• Journal Article Publishing Support Center

To post social content, you must have a display name. The page will refresh upon submission. Any pending input will be lost.

What affiliations should I use?

Authors should use their current or recent affiliation in Author forms, and the affiliation that applied mostly when the manuscript was being prepared/ research was undertaken in the proofs of the paper.

Proof Central makes it possible to change the author list, including the affiliations and the associated footnotes. To do so, click on the 'pencil' icon to open the edit screen in the right pane. Here you can add, remove or edit author names, the author’s associated affiliations and footnotes. Changes made to the author group will always be reviewed and require approval from the journal editor, to make sure no invalid correction is being made by the corresponding author.

Was this answer helpful?

Thank you for your feedback, it will help us serve you better. If you require assistance, please scroll down and use one of the contact options to get in touch.

Help us to help you:

Thank you for your feedback!

• Why was this answer not helpful?
• It was hard to understand / follow.
• It did not answer my question.
• The solution did not work.
• There was a mistake in the answer.
• Feel free to leave any comments below: Please enter your feedback to submit this form

Related Articles:

• What should I do if my organization (i.e. affiliation or funder) is not found when I am requested to add this information when completing the Rights and Access form?
• Author Guide to the Publication Process (Post-Acceptance)
• What should I do if the proof is incorrect or incomplete?
• Will I get to check my proofs again?
• Can I make a correction to my typeset article after I have returned my proofs?

For further assistance:

• - Google Chrome

Intended for healthcare professionals

• Access provided by Google Indexer
• My email alerts
• BMA member login
• Username * Password * Forgot your log in details? Need to activate BMA Member Log In Log in via OpenAthens Log in via your institution

Search form

• Advanced search
• Search responses
• Search blogs
• News & Views
• Research papers should...
• Authors’ affiliations in Research Papers: To Include or not

Rapid response to:

Research papers should omit their authors’ affiliations

• Related content
• Article metrics
• Rapid responses

Rapid Response:

The fact that the affiliation of authors could influence readers/reviewers has been highlighted by Matthew Harris in a Personal View (1). It has also been suggested that research papers should omit their authors’ affiliations. Nevertheless, we assume that, although the presence of authors’ affiliations in the articles could impose the concept that the study is well-conducted or more immediately relevant to the context of the reader, their elimination would violate the freedom of the readers.

Furthermore, when it comes to the medical sciences and the lives that could be either saved thanks to an excellent study or lost due to a fabricated or biased study, the editors and reviewers ought to be more cautious. The emerging discipline of “reverse innovation” is extremely appealing yet it neglects a crucial fact. In case of detection of any conflict, bias, mistake or fabrication in the studies from within developed countries, they are ultimately retracted from the databases; this is not an uncommon phenomenon these days, especially in the leading journals (2, 3). Consequently, the authors shoulder the responsibility; appropriate legislation is ready to be promptly implemented and the losses caused due to the flawed study are compensated to some extents. One might skeptically pose the question whether this would be true for all authors from every corner of the world.

References:

1. BMJ 2014;349:g6439 2. Shafer SL. Editor's Note: Notice of Retraction. Anesth Analg. 2014;119(5):1225. doi: 10.1213/ANE.0000000000000417. 3. Lancet Editors. Retraction--Valsartan in a Japanese population with hypertension and other cardiovascular disease (Jikei Heart Study): a randomised, open-label, blinded endpoint morbidity-mortality study. Lancet. 2013; 7;382(9895):843. doi: 10.1016/S0140-6736(13)61847-4.

Competing interests: No competing interests

Library Research Guides - University of Wisconsin Ebling Library

Uw-madison libraries research guides.

• Course Guides
• Subject Guides
• University of Wisconsin-Madison
• Research Guides
• Publication Tracking
• Searching for an Affiliation in Google Scholar

Publication Tracking : Searching for an Affiliation in Google Scholar

• Getting Started
• Searching for an Individual Author in PubMed
• Searching for a Group of Authors in PubMed
• Searching for an Institution or Department in PubMed
• Searching for an Individual Author in Scopus
• Searching for a Group of Authors in Scopus
• Searching for an Affiliation in Scopus
• Searching for an Individual Author in Google Scholar
• Searching for a Group of Authors in Google Scholar
• Exporting Search Results

This page gives tips on how to search for an affiliation in Google Scholar. Click here to access this information as a downloadable PDF.

Click here to access a PDF containing search templates and examples of searching for an affiliation in Google Scholar.

Constructing Your Search

1. construct a search using affiliation keywords.

Unfortunately Google Scholar does not have a field tag for affiliations. In consequence, you will need to construct your search using affiliation keywords, and combine them with the Boolean OR (or the “|” symbol in Google Scholar), like so:

wisconsin|Madison|UW|wi|wisc

“|” works the same as a Boolean OR would, in that it will be retrieving publications that mention wisconsin, Madison, UW, wi, or wisc, or all of the terms in them.

2. Increase Specificity by Using Quotation Marks

If any of your affiliation keywords are comprised of more than one word, you can use quotation marks to search for the keyword as a phrase. So, for example, searching "young adult" is going to search for that intact phrase, whereas searching young adult, without quotation marks, will look for articles that have young and adult anywhere in the article, regardless of how apart those two words might be in the article (e.g., it could retrieve an article that says, "The young polar bear was now an adult").

So if you wanted to narrow your search to only publications that mention some variation of the University of Wisconsin-Madison, and not just Wisconsin, your search could look something like this:

“University of Wisconsin Madison”|”University of Wisconsin-Madison”|”UW Madison”

This search will only retrieve publications that mention the University of Wisconsin Madison, the University of Wisconsin-Madison, UW Madison, or all of these terms.

3. Limit by Date

You can limit by date by using the date filters on the left-hand side of the page. If you would like to search by a specific date range, you can click “Custom Range.”

Google Scholar has a character limit!

Important : Note that Google Scholar has a limit of only 256 characters for searches

How Do I Interpret These Searches?

Boolean Operators (AND and OR, represented by a space and | in Google Scholar)

OR ("|" in Google Scholar) is used to combine synonyms together. For example, a search of parent|guardian is going to retrieve publications that have the word parent, the word guardian, or both the words parent and guardian in them.

AND (a space in Google Scholar " ") is used to combine concepts together. For example, a search of parent guardian is going to retrieve publications that have BOTH the words parent and guardian in them. If a publication has the word parent, and not the word guardian, your search will not retrieve that publication.

Quotation Marks " "

These tell Google Scholar to search for two or more words as an intact phrase. So, for example, search ing "young adult" is going to search for that intact phrase, whereas search ing young adult, without quotation marks, will look for articles that have young and adult anywhere in the article, regardless of how apart those two words might be in the article (e.g., it could retrieve an article that says, "The young polar bear was now an adult ").

• << Previous: Searching for a Group of Authors in Google Scholar
• Next: Exporting Search Results >>
• Last Updated: Sep 27, 2023 11:47 AM
• URL: https://researchguides.library.wisc.edu/publication-tracking

Journal ratings: a paper affiliation methodology

• Published: 23 June 2021
• Volume 126 , pages 8063–8090, ( 2021 )

Cite this article

• Domingo Docampo 1 &
• Vicente Safón   ORCID: orcid.org/0000-0002-1144-5924 2   nAff3  

784 Accesses

6 Citations

1 Altmetric

Explore all metrics

Journal ratings are a key factor when individuals or institutions assess research and select journals. Despite their significant development in recent years, ratings are still not sufficiently precise or updated enough, depending on the subject and method used. In this paper, we present a new methodology, called paper affiliation index , with which to create subject journal ratings using expert judgment and research impact, both of which are based on secondary, objective measures, thus making it possible to produce lists every year without human manipulation at virtually no cost. We compare the results of our methodology with the selection of top journals by the ShanghaiRanking’s Global Ranking of Academic Subjects in the Social Sciences, the Excellence in Research for Australia, Australian Business Dean Council, the Academic Journal Guide release by Chartered Association of Business Schools lists, and the Journal Impact Factor calculated by Clarivate Analytics .

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Why, When, Who, What, How, and Where for Trainees Writing Literature Review Articles

Gerry L. Koons, Katja Schenke-Layland & Antonios G. Mikos

How to Write and Publish a Research Paper for a Peer-Reviewed Journal

Clara Busse & Ella August

Literature reviews as independent studies: guidelines for academic practice

Sascha Kraus, Matthias Breier, … João J. Ferreira

Some research has put forward mixed methods. For example, Chen and Chen ( 2011 ) proposed the Evolutionary PageRank algorithm, which first uses the PageRank algorithm to assess the journal’s prestige and then uses the Multi-Objective Particle Swarm Optimization method to balance the citation analysis and the experts’ opinions. More recently, Yuan et al. ( 2020 ) have proposed a mixed method where the weights of the objective indicators are obtained from experts’ ratings previously published in ranking lists which were based on opinions. Mixed methods can also be used for pooling different lists to obtain meta-rankings (Herrmann et al., 2011 ).

In Docampo and Safón (in review) we have developed a less sophisticated first version of the PAI methodology for finance journals.

The full results are available upon request.

Retrieved December 25th, 2020 from http://www.shanghairanking.com/subject-survey/survey-methodology-2020.html .

2010 Final Journal List. Retrieved October 27th, 2020 from https://www.righttoknow.org.au/request/journal_list_relating_to_the_201

These percentages are similar to the proportion of top journals in ABDC (A* = 7.4%, ABDC 2019), and AJG (4 and 4* = 7.6%, AJG 2018) lists.

We would like to thank a referee for considering this factor.

ABDC (2019). Australian Business Deans Council 2019 Journal Quality List Review. Final Report 6 December 2019. Retrieved August 25th, 2020 from www.abdc.edu.au .

AJG (2018). Academic Journal Guide 2018. Chartered Association of Business Schools (ABS). Retrieved November 26th, 2020 from www.charteredabs.org

Alexander, J. C., & Mabry, R. H. (1994). Relative significance of journal authors, and articles cited in financial research. Journal of Finance, 49 , 697–712.

Article   Google Scholar  

Alexander, J. K., Scherer, R. F., & Lecoutre, M. (2007). A global comparison of business journal ranking systems. Journal of Education for Business, 82 (6), 321–328.

Arnold, T., Butler, A. W., Crack, T. F., & Altintig, A. (2003). Impact: What influences finance research? Journal of Business, 76 (2), 343–361.

Baccini, A., & De Nicolao, G. (2016). Do they agree? Bibliometric evaluation versus informed peer review in the Italian research assessment exercise. Scientometrics , 108 (3), 1651–1671.

Bajo, E., Barbi, M., & Hillier, D. (2020). Where should I publish to get promoted? A finance journal ranking based on business school promotions. Journal of Banking & Finance, 114 , 105780.

Barman, S., Tersine, R. J., & Buckley, M. R. (1991). An empirical assessment of the perceived relevance and quality of POM-related journals by academicians. Journal of Operations Management, 10 (2), 194–212.

Barnes, S. J. (2005). Assessing the value of IS journals. Communications of the ACM, 48 (1), 110–112.

Baumgartner, H., & Pieters, R. (2003). The structural influence of marketing journals: A citation analysis of the discipline and its subareas over time. Journal of Marketing, 67 (2), 123–139.

Beattie, V., & Goodacre, A. (2006). A new method for ranking academic journals in accounting and finance. Accounting and Business Research, 36 (2), 65–91.

Benati, S., & Stefani, S. (2011). The academic journal ranking problem: A fuzzy-clustering approach. Journal of Classification, 28 (1), 7–20.

Article   MathSciNet   Google Scholar  

Borokhovich, K. A., Lee, A. A., & Simkins, B. J. (2011). A framework for journal assessment: The case of the Journal of Banking and Finance. Journal of Banking & Finance, 35 (1), 1–6.

Brown, L. D., & Huefner, R. J. (1994). The familiarity with and perceived quality of accounting journals: Views of senior accounting faculty in leading US MBA programs. Contemporary Accounting Research, 11 (1), 223–250.

Bryce, C., Dowling, M., & Lucey, B. (2020). The journal quality perception gap. Research Policy, 49 (5), 103957.

Chan, K. C., Chang, C. H., & Chang, Y. (2013). Ranking of finance journals: Some google scholar citation perspectives. Journal of Empirical Finance, 21 , 241–250.

Chan, K. C., Chang, C. H., & Chang, Y. C. (2013). Ranking of finance journals: Some Google Scholar citation perspectives. Journal of Empirical Finance, 21 , 241–250.

Chan, K. C., Chen, C. R., & Steiner, T. L. (2002). Production in the finance literature, institutional reputation, and labor mobility in academia: A global perspective. Financial Management, 31 (4), 131–156.

Chan, K. C., Fung, A., Fung, H. G., & Yau, J. (2016). Ranking of institutions and academic journals. Managerial Finance, 42 (4), 292–302.

Chen, Y. L., & Chen, X. H. (2011). An evolutionary PageRank approach for journal ranking with expert judgements. Journal of Information Science, 37 (3), 254–272.

Cudd, M., & Morris, J. (1988). Bias in journal ratings. Financial Review, 23 (1), 117–125.

Currie, R. R., & Pandher, G. S. (2011). Finance journal rankings and tiers: An active scholar assessment methodology. Journal of Banking and Finance, 35 (1), 7–20.

Currie, R. R., & Pandher, G. S. (2020). Finance journal rankings: Active scholar assessment revisited. Journal of Banking and Finance, 111 , 105717.

Docampo, D., and Safón, V. ( in review ): Finance journal rankings: A paper affiliation methodology. Journal of International Financial Management & Accounting.

Eisend, M. (2011). Is VHB-JOURQUAL2 a good measure of scientific quality? Assessing the validity of the major business journal ranking in German-speaking countries. Business Research, 4 (2), 241–274.

Eleftheriou, K., & Polemis, M. (2020). One list to fit them all: What do we learn from journal ranking? Finance Research Letters, 35 , 101278.

Engemann, K. M., & Wall, H. J. (2009). A journal ranking for the ambitious economist. Federal Reserve Bank of St. Louis Review, 91 (3), 127–139.

Google Scholar  

Ennas, G., Biggio, B., & Di Guardo, M. C. (2015). Data-driven journal meta-ranking in business and management. Scientometrics, 105 (3), 1911–1929.

ERA (2010). Excellence in Research for Australia. Retrieved October 27th, 2020 from https://www.arc.gov.au/excellence-research-australia/era-reports

Extejt, M. M., & Smith, J. E. (1990). The behavioral sciences and management: An evaluation of relevant journals. Journal of Management, 16 (3), 539–551.

Fei, Q., & Bell, R. (2013). Marketing journal ranking, celebrity authors, and the diminishing quality gap. Marketing journal ranking, celebrity authors, and the diminishing quality gap. Mustang Journal of Management and Marketing, 3 , 16–30.

Gordon, M. D. (1982). Citation ranking versus subjective evaluation in the determination of journal hierarchies in the social sciences. Journal of the American Society for Information Science, 33 (1), 55–57.

Gorman, M. F., & Kanet, J. J. (2005). Evaluating operations management-related journals via the author affiliation index. Manufacturing and Service Operations Management, 7 (1), 3–19.

GRAS (2020). Academic Ranking of World Universities: Methodology for ShanghaiRanking's Global Ranking of Academic Subjects 2020. Retrieved November 26th, 2020, from http://www.shanghairanking.com/Shanghairanking-Subject-Rankings/Methodology-for-ShanghaiRanking-Global-Ranking-of-Academic-Subjects-2020.html .

Guidry, J. A., Guidry Hollier, B. N., Johnson, L., Tanner, J. R., & Veltsos, C. (2004). Surveying the cites: A ranking of marketing journals using citation analysis. Marketing Education Review, 14 (1), 45–59.

Guo, H., Wang, B., Qiao, X., & Liu, R. (2016). A review of studies on citations and journal ranking in finance. Managerial Finance, 42 (4), 303–311.

Haddawy, P., Hassan, S. U., Asghar, A., & Amin, S. (2016). A comprehensive examination of the relation of three citation-based journal metrics to expert judgment of journal quality. Journal of Informetrics, 10 (1), 162–173.

Harzing, A. (2020). Journal quality list. Retrieved August 25th, 2020 from www.harzing.com .

Heckman, J. J., & Moktan, S. (2020). Publishing and promotion in economics: The tyranny of the top five. Journal of Economic Literature, 58 (2), 419–470.

Herrmann, R., Berg, E., Dabbert, S., Pöchtrager, S., & Salhofer, K. (2011). Going beyond impact factors: A survey-based journal ranking by agricultural economists. Journal of Agricultural Economics, 62 (3), 710–732.

Hirschberg, J. G., & Lye, J. N. (2020). Grading journals in economics: The ABCs of the ABDC. Journal of Economic Surveys, 34 (4), 876–921.

Horowitz, I. (2003). Preference-neutral attribute weights in the journal-ranking problem. Journal of the Operational Research Society, 54 (5), 452–457.

Article   MATH   Google Scholar  

Jaafar, R., Pereira, V., Saab, S. S., & El-Kassar, A. N. (2020). Which journal ranking list? A case study in business and economics. EuroMed Journal of Business.

Jarvis, R. M., & Coleman, P. (2007). Ranking law reviews by author prominence-Ten years later. Law Library Journal, 99 , 573–588.

Jarvis, R. M., & Coleman, P. G. (1997). Ranking law reviews: An empirical analysis based on author prominence. Arizona Law Review, 39 , 15–23.

Kao, E. H., Hsu, C. H., Lu, Y., & Fung, H. G. (2016). Ranking of finance journals: A stochastic dominance analysis. Managerial Finance, 42 (4), 312–323.

Katerattanakul, P., Han, B., & Hong, S. (2003). Objective quality ranking of computing journals. Communications of the ACM, 46 (10), 111–114.

Krippendorff, K. (2004). Content analysis: An introduction to its methodology . Sage.

Krippendorff, K. (2011). Computing Krippendorff's Alpha-Reliability. Retrieved August 25th, 2020 from http://repository.upenn.edu/asc_papers/43

Koojaroenprasit, N., Weinstein, A., Johnson, W. C., & Remington, D. O. (1998). Marketing journal rankings revisited: Research findings and academic implications. Marketing Education Review, 8 (1), 95–102.

Koseoglu, M. A. (2018). A new approach to journal ranking: Social structure in hospitality and tourism journals. International Journal of Contemporary Hospitality Management, 32 (2), 389–424.

Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33 (1), 159–174.

Larivière, V., & Gingras, Y. (2010). The impact factor’s Matthew effect: A natural experiment in bibliometrics. Journal of the American Society for Information Science and Technology, 61 (2), 424–427.

Lowry, P. B., Gaskin, J., Humpherys, S. L., Moody, G. D., Galletta, D. F., Barlow, J. B., et al. (2013). Evaluating journal quality and the association for information systems senior scholars' journal basket via bibliometric measures: Do expert journal assessments add value? MIS quarterly , 37 (4), 993–1012.

Mabry, R. H., & Sharplin, A. D. (1985). The relative importance of journals used in financial research. Journal of Financial Research, 8 , 287–296.

Mingers, J., & Harzing, A. W. (2007). Ranking journals in business and management: A statistical analysis of the Harzing data set. European Journal of Information Systems, 16 (4), 303–316.

Mingers, J., & Yang, L. (2017). Evaluating journal quality: A review of journal citation indicators and ranking in business and management. European Journal of Operational Research, 257 (1), 323–337.

Article   MathSciNet   MATH   Google Scholar  

Moosa, I. (2011). The demise of the ARC journal ranking scheme: An ex post analysis of the accounting and finance journals. Accounting & Finance, 51 (3), 809–836.

Moosa, I. A. (2016). A critique of the bucket classification of journals: The ABDC list as an example. Economic Record, 92 (298), 448–463.

Moussa, S., & Touzani, M. (2010). Ranking marketing journals using the google scholar-based hg-index. Journal of Informetrics, 4 (1), 107–117.

Peffers, K., & Ya, T. (2003). Identifying and evaluating the universe of outlets for information systems research: Ranking the journals. Journal of Information Technology Theory and Application, 5 (1), 63–84.

Peters, K., Daniels, K., Hodgkinson, G. P., & Haslam, S. A. (2014). Experts’ judgments of management journal quality: An identity concerns model. Journal of Management, 40 (7), 1785–1812.

Petersen, C. G., Aase, G. R., & Heiser, D. R. (2011). Journal ranking analyses of operations management research. International Journal of Operations and Production Management., 31 (4), 405–422.

Podsakoff, P. M., MacKenzie, S. B., Bachrach, D. G., & Podsakoff, N. P. (2005). The influence of management journals in the 1980s and 1990s. Strategic Management Journal, 26 (5), 473–488.

Rahal, A., & Zainuba, M. (2019). The rating dilemma of academic management journals: Attuning the perceptions of peer rating. Advances in Business Research, 9 (1), 26–45.

Ren, L. Y. L. (2016). A note about the finance journal rankings and citation counts. Asian Academy of Management Journal of Accounting and Finance, 12 (Suppl. 1), 183–194.

Saladin, B. (1985). Operations management research: Where should we publish. Operations Management Review, 3 (4), 3–9.

Serenko, A., & Bontis, N. (2013). First in, best dressed: The presence of order-effect bias in journal ranking surveys. Journal of Informetrics, 7 (1), 138–144.

Serenko, A., & Dohan, M. (2011). Comparing the expert survey and citation impact journal ranking methods: Example from the field of artificial intelligence. Journal of Informetrics, 5 (4), 629–648.

Sheskin, D. J. (2003). Handbook of parametric and nonparametric statistical procedures . Chapman and Hall.

Book   MATH   Google Scholar  

Soteriou, A. C., Hadjinicola, G. C., & Patsia, K. (1999). Assessing production and operations management related journals: the European perspective. Journal of Operations Management, 17 (2), 225–238.

Steward, M. D., & Lewis, B. R. (2010). A comprehensive analysis of marketing journal rankings. Journal of Marketing Education, 32 (1), 75–92.

Swidler, S., & Goldreyer, E. (1998). The value of a finance journal publication. Journal of Finance, 53 , 351–363.

Tadajewski, M. (2016). Academic labour, journal ranking lists and the politics of knowledge production in marketing. Journal of Marketing Management, 32 (1–2), 1–18.

Tahai, A., & Meyer, M. J. (1999). A revealed preference study of management journals’ direct influences. Strategic Management Journal, 20 (3), 279–296.

Vanclay, J. K. (2011). An evaluation of the Australian Research Council’s journal ranking. Journal of Informetrics, 5 (2), 265–274.

Vogel, R., Hattke, F., & Petersen, J. (2017). Journal rankings in management and business studies: What rules do we play by? Research Policy, 46 (10), 1707–1722.

Wilhite, A. W., & Fong, E. A. (2012). Coercive citation in academic publishing. Science, 335 (6068), 542–543.

Wilsdon, J., et al. (2015). The metric tide: Report of the independent review of the role of metrics in research assessment and management. https://doi.org/10.13140/RG.2.1.4929.1363

Wouters, P. et al. (2015). The Metric Tide: Literature Review (Supplementary Report I to the Independent Review of the Role of Metrics in Research Assessment and Management). HEFCE. DOI: https://doi.org/10.13140/RG.2.1.5066.3520

Xu, N., Chan, K. C., & Chang, C. H. (2016). A quality-based global assessment of financial research. Review of Quantitative Finance and Accounting, 46 (3), 605–631.

Xu, F., Liu, W., & Rousseau, R. (2015). Introducing sub-impact factor (SIF-) sequences and an aggregated SIF-indicator for journal ranking. Scientometrics, 102 (2), 1577–1593.

Yuan, L., Li, J., Li, R., Lu, X., & Wu, D. (2020). Mapping the evaluation results between quantitative metrics and meta-synthesis from experts’ judgements: Evidence from the supply chain management and logistics journals ranking. Soft Computing, 24 (9), 6227–6243.

Yuyuenyongwatana, R. P., & Carraher, S. M. (2008). Academic journal ranking: Important to strategic management and general management researchers? Journal of Business Strategies, 25 (2), 1–9.

Zainuba, M., & Rahal, A. (2015). Assessing the validity of business and management journals ranking list: An alternative approach for determining journal quality. Annals of Management Science, 4 (2), 1–28.

Download references

Author information

Vicente Safón

Present address: , Ivie, Spain

Authors and Affiliations

Atlantic Research Center for Information and Communication Technologies, University of Vigo, Vigo, Spain

Domingo Docampo

Department of Management, University of Valencia, Valencia, Spain

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Vicente Safón .

Rights and permissions

Reprints and permissions

About this article

Docampo, D., Safón, V. Journal ratings: a paper affiliation methodology. Scientometrics 126 , 8063–8090 (2021). https://doi.org/10.1007/s11192-021-04045-3

Download citation

Received : 10 February 2021

Accepted : 15 May 2021

Published : 23 June 2021

Issue Date : September 2021

DOI : https://doi.org/10.1007/s11192-021-04045-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Journal ratings
• Journal rankings
• Social sciences
• Find a journal
• Publish with us
• Track your research

Research Voyage

Research Tips and Infromation

7 Essential Steps for Changing Author Affiliation in Research Paper

As researchers traverse their academic journey, their affiliations may undergo changes due to new opportunities, collaborations, or career advancements. While the process of publishing research papers often involves meticulous attention to detail, unforeseen circumstances can occasionally lead to inaccuracies in affiliations associated with a published paper.

As a researcher, you might have encountered situations where you needed to update your affiliation on an already published research paper. Whether it’s joining a new institution, relocating to a different country, or transitioning to a different research group, it’s essential to ensure that your affiliations accurately reflect your current standing in the academic community. In this blog post, we aim to provide a comprehensive guide on how to navigate the process of changing affiliations in already published research papers.

Emphasizing the importance of adhering to publication policies and maintaining accuracy in scientific literature, this guide will walk you through the step-by-step process of correcting affiliations. We’ll explore how to initiate the correction process, gather the necessary documentation, and interact with journal publishers in a professional manner. Furthermore, we’ll touch upon considerations for more substantial changes and address the significance of updating personal profiles and notifying indexing services.

By sharing insights and best practices, we hope to empower researchers to navigate the affiliation change process smoothly and responsibly. Ultimately, this blog post aims to contribute to the integrity and reliability of scientific literature, ensuring that affiliations accurately represent the journeys and contributions of researchers in the ever-evolving realm of academia.

Let’s delve into the intricacies of updating affiliations in published research papers, and equip ourselves with the knowledge to make necessary corrections with confidence and efficiency.

Introduction

Importance of understanding publication guidelines:, tips on where to find publication guidelines:, types of documentation for affiliation change:, advice on contacting the journal or publisher:, process of submitting a correction or erratum:.

• Journal's Role in Reviewing the Correction or Erratum:

Emphasizing the Need for Accuracy and Validity:

Importance of notifying indexing services:, informing indexing services:, advice on updating personal profiles:, request letter for affiliation change in published paper.

Research papers are the currency of knowledge dissemination, and the affiliations listed on these papers carry significant weight. Affiliations serve as a vital identifier, linking researchers to their respective institutions or organizations, and they play a crucial role in establishing credibility, recognizing contributions, and fostering collaborations within the scientific community.

When a research paper is published, the affiliations of the authors are essentially imprinted in time, forever associated with the findings and insights presented in the work. However, the journey of a researcher is dynamic, and circumstances can change over time. Researchers may find themselves faced with situations where their affiliations need to be updated in already published papers. These situations can arise due to a variety of reasons, such as career advancements, relocation to a new institution, or joining a collaborative project with colleagues from different organizations.

Imagine a scenario where Dr. Smith, an esteemed biologist, publishes a groundbreaking study on genetic mutations in cancer cells. The paper, which carries her former institution’s affiliation, receives widespread recognition and becomes a cornerstone in cancer research. However, a year after the publication, Dr. Smith accepts an enticing research opportunity at a leading medical centre. Now, with her scientific journey taking her to a new institution, she realizes the need to update her affiliation on the already published paper, ensuring that her latest work reflects her current professional standing.

In such cases, ensuring accurate and up-to-date affiliations is not only a matter of personal career progression but also a matter of scientific integrity. It’s crucial to maintain an accurate historical record of affiliations, as these affiliations provide valuable insights into the collaborative networks and institutional contributions that shaped the research landscape.

In this blog post, we aim to shed light on the significance of affiliations in research papers and why researchers may need to modify them post-publication. We recognize the challenges researchers might face when attempting to make such changes and the potential impact on their academic standing and future collaborations. To empower researchers in navigating this process, we will provide a step-by-step guide, offering practical advice on how to initiate and implement affiliation changes in published papers.

Whether you’re a seasoned researcher or a budding scholar, understanding the process of updating affiliations is essential for maintaining the accuracy and credibility of your scientific contributions. So, let’s embark on this informative journey and equip ourselves with the knowledge and tools to responsibly manage affiliation changes in the dynamic world of academic research.

I have written articles on the possible corrections an author may want to do to a research paper that is already published. Please visit the articles listed below for further details.

• “ 5 Proven Steps to Change Author Email Id in a Published Research Paper”
• “ How to Change Author Name on a Previously Published Research Paper? “
• “ 4 Easy Steps to Withdraw Author Name from a Research Paper “

How can I change my affiliation in a Published Research Paper?

Step 1: check publication policy.

Before embarking on the process of changing affiliations in an already published research paper, it is vital to familiarize yourself with the publication’s guidelines and policies regarding corrections and updates. Each journal or publication may have specific procedures and requirements for making changes to author affiliations, and understanding these guidelines is crucial to ensure a smooth and successful process.

Journal publications uphold rigorous standards of accuracy and integrity to maintain the credibility of scientific literature. Affiliation details play a significant role in establishing the author’s credibility, expertise, and institutional association with the research presented in the paper. Any changes to affiliations should be done in a manner that preserves the historical record of the paper while reflecting the most current and accurate information.

Let’s consider an example where Dr. Johnson, a researcher in the field of environmental science, published a paper on climate change with her previous institution’s affiliation. Due to her recent appointment as a faculty member at a renowned university, Dr. Johnson now wishes to update the affiliation on the published paper to reflect her current position. However, she is unsure about the correct procedure to follow.

• Journal Website : The publication’s website often contains a dedicated section titled “Instructions for Authors” or “Author Guidelines.” This section outlines the journal’s policies, including instructions on how to correct or update affiliations post-publication.
• Author’s Dashboard : If the paper was submitted through an online manuscript submission system, the author’s dashboard may provide information on how to request affiliation changes.
• Contact the Editorial Office : If the journal’s guidelines are not easily accessible, contacting the editorial office via email or phone is a reliable way to obtain the necessary information. Editorial staff members are well-versed in the publication’s policies and can provide guidance on making affiliation changes.
• Online Resources : Some journals have FAQs or online resources addressing common author queries, including how to correct affiliations. Check the journal’s website or relevant forums for any such resources.
• Publication Agreement : Revisit the publication agreement or copyright transfer form you signed during the submission process. It may contain provisions related to post-publication changes.

Example (Continued):

After some research, Dr. Johnson visits the journal’s website and locates the “Instructions for Authors” page. She discovers a subsection specifically addressing corrections and updates to published papers. Following the provided instructions, she prepares to contact the editorial office to initiate the process of changing her affiliation.

By understanding the publication’s guidelines and following the correct procedure, researchers like Dr. Johnson can navigate the affiliation change process with confidence, ensuring that their contributions are accurately represented in the scientific literature. Remember, each journal may have its own unique guidelines, so it’s essential to be diligent in locating and adhering to the specific instructions for the paper in question.

Step 2: Gather Documentation

Once you have familiarized yourself with the publication’s guidelines and determined the appropriate procedure for changing affiliations, the next crucial step is to gather the necessary documentation to support the affiliation change. Providing proper documentation is essential to validate the updates and ensure the accuracy and credibility of the revised affiliation.

• Official Letters from the New Institution : A formal letter from the new institution confirming your affiliation with them is one of the primary and most important documents. The letter should be on the institution’s official letterhead and signed by an authorized representative, such as the department head, dean, or human resources officer. The letter should include your name, the effective date of the affiliation change, your official title or position at the new institution, and any other relevant details.

Let’s consider Dr. Rodriguez, a postdoctoral researcher in the field of neuroscience, who recently accepted a position at a prestigious research institute. She now needs to update her affiliation on a published paper that was submitted during her previous postdoctoral position. To support the affiliation change, Dr. Rodriguez obtains an official letter from the research institute confirming her employment and new affiliation. The letter contains all the necessary details, including the effective date of the change.

• Employment Contract or Offer Letter : If your affiliation change is due to a new job or employment opportunity, providing a copy of your employment contract or offer letter can be valuable documentation. This document further substantiates the official nature of your affiliation with the new institution and reinforces the validity of the update.
• Acceptance Letters or Invitations to Collaborate : In cases where the affiliation change is the result of a collaboration with researchers from a different institution, you can include acceptance letters or invitations to collaborate as additional supporting documentation. These letters should clearly state the nature of the collaboration and your role in the project.
• Publication Agreement : Including a copy of the publication agreement or copyright transfer form you signed during the initial submission can serve as proof that you are an author associated with the paper.
• CV or Resume : While not a formal document for the affiliation change process, providing an updated CV or resume that includes your new affiliation can be helpful for the editorial office to cross-check and verify the change.

Having received the official letter from the prestigious research institute, Dr. Rodriguez is now ready to initiate the affiliation change process. She gathers all the relevant documentation, including the official letter, her new employment contract, and a copy of the publication agreement signed during the initial submission.

By compiling the necessary documentation, researchers like Dr. Rodriguez ensure that their affiliation change request is well-substantiated and meets the publication’s requirements for validation. Proper documentation adds credibility to the affiliation change, giving the journal’s editorial office confidence in implementing the updates accurately. Remember to provide clear and legible copies of the documents to avoid any delays or complications in the process.

Step 3: Contact the Journal or Publisher

After gathering the necessary documentation to support the affiliation change, the next step is to contact the journal or publisher to initiate the process formally. Professional and courteous communication is essential when reaching out to the editorial office to ensure smooth and efficient handling of your request.

• Locating Contact Information : Start by identifying the appropriate contact information for the journal or publisher. Most reputable journals will have a dedicated editorial office or a contact email specifically for author inquiries or corrections. You can typically find this information on the journal’s website, in the published paper, or in any communications you may have received from the journal during the review process.
• Compose a Clear and Concise Email : When drafting your email, be clear and concise in stating the purpose of your inquiry. Begin by mentioning the title of the published paper, the names of all authors, and the DOI (Digital Object Identifier) or any other identifying information of the paper.

Subject: Request for Affiliation Change – Paper Title: “Advancements in Neural Network Research”

Dear [Journal/Publisher Name],

I hope this email finds you well. I am writing to request a correction to the affiliation associated with the published paper titled “Advancements in Neural Network Research,” authored by [Author Names]. The DOI for the paper is [DOI number].

• Explain the Reason for the Affiliation Change : Briefly explain the reason for the affiliation change and attach the relevant supporting documentation. State the effective date of the affiliation change and provide a clear statement of the updated affiliation details.

As of [Effective Date], I have joined [New Institution Name] as [New Position/Title]. I kindly request to update my affiliation on the published paper to reflect this change accurately. Please find attached the official letter from [New Institution Name] confirming my affiliation with them.

• Express Gratitude and Professionalism : Show appreciation for the journal’s consideration of your request and maintain a professional tone throughout the email.

I understand that the editorial process involves careful attention to detail, and I genuinely appreciate your assistance in making this important correction. Should you require any additional information or have any questions, please do not hesitate to reach out to me.

Thank you for your time and attention to this matter.

Sincerely, [Your Name] [Your Current Affiliation]

• Attach Relevant Documents : Attach the supporting documentation, such as the official letter from the new institution, your updated CV, or any other documents requested by the journal’s guidelines.

Dr. Rodriguez drafts a professional email following the guidelines outlined above. She attaches the official letter from the prestigious research institute and includes her updated CV for reference. After thoroughly reviewing the email for clarity and accuracy, she sends it to the contact email provided by the journal.

By communicating professionally and providing all the necessary information, researchers like Dr. Rodriguez can ensure that their affiliation change request is handled efficiently by the journal’s editorial office. Remember to be patient during this process, as it may take some time for the journal to review and process the request, especially if there are other pending corrections or updates.

Step 4: Submit a Correction or Erratum

Once the journal or publisher has acknowledged your request to change the affiliation on the published paper and provided instructions for correction, it’s time to prepare and submit a formal correction or erratum. A correction is issued to rectify errors or inaccuracies in the published paper, while an erratum is used to address mistakes made by the journal itself.

• Identify the Corrected Information : Clearly state the specific changes that need to be made to the affiliations. Include the updated affiliation details, including the new institution’s name, department, address, and any additional information required by the journal’s guidelines.

The corrected affiliation for Dr. Johnson is as follows: Department of Environmental Science, Prestigious Research Institute, City, Country.

• Title the Correction or Erratum : Use a descriptive title that indicates that the document is a correction or erratum for the published paper. Include the paper’s title and any relevant identifying information, such as the DOI or publication date.

Correction to: “Insights into Climate Change Impact on Biodiversity” – DOI: [DOI number]

• Explain the Reason for the Correction or Erratum : Provide a concise explanation of the reason for the change in affiliations. Mention that the original publication had an outdated affiliation and that this correction aims to update and accurately reflect the author’s current institutional affiliation.

We are issuing this correction to update the author’s affiliation on the published paper to reflect her current position at the Prestigious Research Institute. The previous affiliation listed was based on her previous postdoctoral position.

• Reference the Original Paper : Include the full citation or reference to the original published paper that requires the correction or erratum. This will help readers and indexers connect the corrected version to the original work.

Original Paper: [Author Names]. (Year). “Insights into Climate Change Impact on Biodiversity.” Journal of Environmental Science, Volume(X), Page Range. DOI: [DOI number]

• Attach Supporting Documentation : Include the relevant supporting documentation that validates the affiliation change. Attach the official letter from the new institution or any other documents required by the journal’s guidelines.

Following the journal’s instructions, Dr. Johnson prepares the correction document. She includes the updated affiliation information, the title indicating the document as a correction, and a concise explanation of the reason for the change. Dr. Johnson references the original paper with its full citation and attaches the official letter from the prestigious research institute.

• Submit the Correction or Erratum : Follow the journal’s specific instructions for submission. Some journals may have a dedicated online platform for corrections or errata, while others may require submission via email.

Dr. Johnson submits the correction document, along with the required attachments, through the journal’s online submission system as per their guidelines.

By submitting a well-organized and clear correction or erratum document, researchers like Dr. Johnson ensure that the journal’s readership and indexing services have access to the accurate and updated affiliation information. This process upholds the integrity of the published scientific literature and ensures that researchers’ contributions are appropriately recognized with their current institutional affiliations.

Step 5: Review and Approval

After submitting the correction or erratum to the journal, the document undergoes a review process to ensure its accuracy and validity. The journal’s role in this step is essential as they act as gatekeepers of scientific integrity, maintaining the credibility of the published literature.

Journal’s Role in Reviewing the Correction or Erratum:

• Verification of Information : The journal’s editorial team carefully reviews the submitted correction or erratum to verify the accuracy of the requested changes. They cross-reference the provided documentation with the original publication and ensure that the updated affiliation information aligns with the supporting evidence.

In Dr. Johnson’s case, the journal’s editorial team compares the correction document with the original paper titled “Insights into Climate Change Impact on Biodiversity” to validate the affiliation change from her previous institution to the prestigious research institute.

• Adherence to Publication Policies : The journal’s editorial team ensures that the correction or erratum complies with the publication’s policies and guidelines. They confirm that the document follows the correct formatting, includes the necessary information, and adheres to ethical standards.

The journal confirms that Dr. Johnson’s correction document includes all the required elements, such as the corrected affiliation, a clear explanation of the change, and a reference to the original paper. They also verify that the supporting documentation provided by Dr. Johnson meets the journal’s requirements.

• Communication with the Author : If any discrepancies or questions arise during the review process, the journal’s editorial team may communicate with the author to seek clarification or additional information. Open communication helps ensure the accuracy and completeness of the correction or erratum.

The journal contacts Dr. Johnson to inquire about a minor formatting issue in the correction document. Dr. Johnson promptly addresses the matter, providing the necessary adjustments.

• Approval and Publication : Once the review process is complete, and the correction or erratum is deemed accurate and valid, the journal approves the document for publication. The updated affiliation is then published in a subsequent issue, either as a standalone correction or as part of an erratum section.

After conducting a thorough review and confirming the validity of Dr. Johnson’s correction document, the journal’s editorial team approves it for publication. The corrected affiliation of Dr. Johnson is scheduled to be published in the upcoming issue of the journal.

It is crucial to underscore that accuracy and validity are paramount when making corrections or issuing errata. The journal’s role in reviewing and approving such changes ensures that the scientific record remains reliable and up-to-date. By maintaining strict quality control measures, journals safeguard against potential inaccuracies and contribute to the integrity of the research community.

As researchers, authors, and readers, we share the collective responsibility to uphold the accuracy of published work. Collaboration between authors and journal teams in the correction process reinforces the commitment to transparent and accurate scientific communication. With these rigorous standards in place, the scientific literature continues to be a reliable foundation for advancing knowledge and shaping the future of research.

Step 6: Notify Indexing Services (if applicable)

After the correction or erratum has been approved and published by the journal, it is essential to notify indexing services about the affiliation change. Indexing services, such as PubMed, Web of Science, Scopus, and others, play a crucial role in organizing and providing access to the vast amount of scientific literature. Informing them about the affiliation change ensures that the updated information is accurately reflected in their databases, facilitating proper attribution and discoverability of the research.

• Accurate Attribution : Indexing services use affiliations to attribute research to specific institutions or organizations accurately. Keeping this information up to date is essential to ensure that researchers are credited appropriately for their work and that institutions receive proper recognition for their contributions.

When Dr. Rodriguez’s affiliation is updated to the prestigious research institute in the journal’s published correction, notifying indexing services like PubMed about this change ensures that her research contributions are accurately linked to her new institution in their database. This allows other researchers and institutions to recognize her affiliation with the prestigious research institute when accessing her publications.

• Discoverability and Accessibility : Correctly indexed affiliations help researchers and readers easily discover relevant literature from specific institutions or researchers. This enhances the accessibility and visibility of research from particular institutions or research groups.

If a reader searches for publications from the prestigious research institute, the correct indexing of Dr. Rodriguez’s research under her new affiliation will lead to more accurate and relevant search results, making it easier for readers to find her latest work.

• Research Evaluations and Rankings : Some institutions and funding agencies use publication records to assess research productivity and impact. Ensuring accurate affiliations is crucial for fair evaluations and rankings, which can influence funding decisions and institutional recognition.

The prestigious research institute’s ranking and reputation may be positively affected by the accurate affiliation indexing of its researchers, such as Dr. Rodriguez. This can lead to increased opportunities for research funding and collaborations.

Each indexing service has its own procedures for updating affiliations. It may involve contacting the indexing service directly, filling out a form, or following specific instructions on their website. Journals or publishers might also have direct communication channels with indexing services to facilitate such updates.

After the publication of the correction with Dr. Rodriguez’s updated affiliation, the journal’s editorial team takes the initiative to notify indexing services about the change. They ensure that the corrected information is communicated accurately to the relevant indexing databases.

By proactively notifying indexing services about affiliation changes, journals, researchers, and institutions contribute to maintaining the accuracy and integrity of research records worldwide. It also ensures that researchers receive proper recognition and that their contributions are accurately represented in the scientific community.

Step 7: Update Personal Profiles

After the affiliation change has been approved and published in the journal, it is crucial for the author to update their personal profiles to reflect the new affiliation. This step helps maintain consistency across various platforms and ensures that the author’s current institutional association is accurately represented in the academic community.

• Researcher Profile Websites : If you have a researcher profile on platforms like ResearchGate, Academia.edu , Google Scholar , or ORCID , log in to your account and update your affiliation information.

Dr. Smith, who recently changed her affiliation to a new university, visits her ResearchGate profile and updates the “Affiliation” section with her new institution’s details. This change is automatically reflected on her ResearchGate profile, which is viewed by researchers worldwide.

• University/Institution Websites : If your new institution hosts researcher profiles on its website, update your affiliation information there as well. This ensures that your profile is consistent with the official records of your institution.

Dr. Johnson, who is now affiliated with the prestigious research institute, visits the institute’s website and navigates to her faculty profile. She updates the “Affiliation” field on her profile page, providing her new position and affiliation details.

• Social and Professional Networking Sites : Platforms like LinkedIn are widely used for professional networking. Make sure to update your LinkedIn profile to reflect the correct affiliation, as this information is visible to potential collaborators, employers, and colleagues.

Dr. Rodriguez, now affiliated with the prestigious research institute, logs in to her LinkedIn account and edits her “Experience” section, adding her new position and affiliation. This update is visible to her connections and professional network.

• Publication Records : If you maintain a personal publication list on your website or other platforms, update the affiliation information for your published papers to match the corrected version in the journal.

Dr. Smith manages her personal website, where she maintains a list of her publications. She updates the affiliation for the published paper to reflect her new institution and provides a link to the corrected version of the paper on the journal’s website.

• Provide Links to the Corrected Paper : When updating your personal profiles, consider providing links to the corrected version of the published paper, especially if it is available online. This allows readers and colleagues to access the accurate and updated version of your work.

Dr. Johnson updates her ResearchGate profile and includes a link to the corrected version of her paper, “Advancements in Neural Network Research,” on the journal’s website. This way, readers who visit her profile can access the most recent and accurate information about her research.

By updating personal profiles with the correct affiliation and providing links to the corrected version of the published paper, researchers ensure that their professional information is current and consistent across different platforms. This contributes to establishing a reliable and accurate academic identity, allowing colleagues and collaborators to find and connect with them easily.

Subject: Request for Affiliation Change in Published Paper

I hope this email finds you well. I am writing to request a correction to the affiliation associated with the published paper titled “[Paper Title]” authored by [Author Names]. The paper’s DOI is [DOI number], and it was published in [Journal Name], [Volume], [Issue], [Publication Year].

I recently experienced a change in my institutional affiliation, and I wish to update the information in the published paper to reflect my current position. The correction is necessary to ensure the accuracy and credibility of the scientific literature and to properly credit my research contributions to the institution with which I am currently affiliated.

I kindly request to update my affiliation as follows:

Old Affiliation: [Old Institution Name], [Old Department], [Old City], [Old Country]

New Affiliation: [New Institution Name], [New Department], [New City], [New Country]

To support this affiliation change, I have attached an official letter from [New Institution Name] confirming my current association with them. The letter is on the institution’s official letterhead and is signed by [Name and Designation of Authorized Representative].

I assure you that the affiliation change has no impact on the content, results, or conclusions presented in the published paper. All co-authors have been informed of this request, and they fully support this correction.

Please let me know if you require any additional information or documentation to proceed with the affiliation change process. I am more than willing to provide any further details necessary.

Thank you for your attention to this matter. I greatly appreciate your cooperation in updating my affiliation in the published paper. I look forward to your positive response.

[Your Name] [Your Current Affiliation] [Contact Email] [Contact Phone Number]

The journey of academic research is one paved with innovation, collaboration, and growth. As researchers, our affiliations serve as critical milestones, connecting us to the institutions and organizations that shape our contributions to the scientific community. However, the dynamic nature of our careers can lead to situations where updating affiliations in already published research papers becomes necessary.

In this comprehensive guide, we have explored the step-by-step process of changing affiliations in published papers, emphasizing the importance of accuracy, transparency, and integrity. Understanding the publication’s guidelines, gathering the right documentation, and maintaining professional communication with the journal’s editorial team are the initial keystones to navigating this process.

We have witnessed the significance of notifying indexing services to ensure accurate records, enhance discoverability, and preserve the credit and recognition researchers deserve. Updating personal profiles with the correct affiliations reinforces a consistent and reliable academic identity, making it easier for colleagues and collaborators to connect and engage.

Upcoming Events

• Visit the Upcoming International Conferences at Exotic Travel Destinations with Travel Plan
• Visit for  Research Internships Worldwide

Recent Posts

• How to End Your Academic/Research Internship?
• PhD or Industry Job? A Comprehensive Career Guide
• Post Doc Positions in India
• 04 Reasons for Outsourcing Academic Conference Management
• How to Put Research Grants on Your CV ?
• All Blog Posts
• Research Career
• Research Conference
• Research Internship
• Research Journal
• Research Tools
• Uncategorized
• Research Conferences
• Research Journals
• Research Grants
• Internships
• Research Internships
• Email Templates
• Conferences
• Blog Partners
• Privacy Policy

Copyright © 2024 Research Voyage

Design by ThemesDNA.com

• Search Menu
• Advance articles
• Author Guidelines
• Submission Site
• Open Access
• Why Publish?
• About Science and Public Policy
• Editorial Board
• Advertising and Corporate Services
• Journals Career Network
• Self-Archiving Policy
• Dispatch Dates
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

• 1. Introduction
• 2. Background and conceptual framework
• 3. Data and methods
• 5. Discussion and conclusion
• Supplementary data
• Conflict of interest statement:
• Acknowledgements
• Appendix A. Response analysis
• Appendix B. Factor analysis and robustness tests
• Appendix C. Correlation matrix
• < Previous

What is behind multiple institutional affiliations in academia?

• Article contents
• Figures & tables
• Supplementary Data

Hanna Hottenrott, Cornelia Lawson, What is behind multiple institutional affiliations in academia?, Science and Public Policy , Volume 49, Issue 3, June 2022, Pages 382–402, https://doi.org/10.1093/scipol/scab086

• Permissions Icon Permissions

Multiple institutional affiliations (or co-affiliations) occur when an academic belongs to more than one organisation. Recent research shows an increase in academics with multiple affiliations, but evidence on how these are organised and on academics’ motivations is mainly anecdotal. In this study we develop a typology of co-affiliations, which identifies four types based on their purpose and origin. We draw on results from a unique international survey of academics in three major science nations (the UK, Germany, and Japan) to study the different factors that could explain the four types of co-affiliations. The analysis shows that academics’ motivations (networking/prestige, resources, teaching, or personal income) correlate with the observed co-affiliation type. Researcher-initiated and research-focussed co-affiliations are often motivated by networking and resource access while co-affiliations that serve other than research purposes are more often income-motivated.

Email alerts

Citing articles via.

• Recommend to your Library

Affiliations

• Online ISSN 1471-5430
• Print ISSN 0302-3427
• Copyright © 2024 Oxford University Press
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

• Future Students
• Current Students
• International
• Researchers
• Student Portal
• Our profile
• Engage with us
• Study with us
• Performance
• Research support
• Funding and grants
• Supervision
• Integrity, Ethics and Compliance
• Research Classifications

Author affiliations

A "byline" or "affiliation" is an acknowledgment of the organisation that has supported you to conduct your research, and should be recorded on the published version of your output.

Why do I need a Charles Sturt University byline?

Acknowledgement of Charles Sturt University is a requirement of the  Authorship Policy (items 42-44) and applies to academic staff, adjuncts, HDR candidates, general staff, and visiting scholars. Affiliations are also used for internal reporting purposes to allocate credit to the organisational unit (Faculty/School/Centre) that supports you, and may have an impact on whether you and your outputs can be included in external submissions such as the Excellence in Research for Australia (ERA). Outputs published prior to or outside of your Charles Sturt University association are exempt from this requirement.

Where do I record the byline?

Depending on the output type or publishing outlet, this may be next to the list of author names at the start or end of the output, or in separate contributor information page(s) or author biographies sections of the publication. You may be asked to nominate your affiliation on your publication consent form or via communications from the publisher or editor of the publication.

What if I am affiliated with multiple institutions?

You do not need to make a choice about where your acknowledgment must go. As long as Charles Sturt University is acknowledged on the output (if appropriate), there is no restriction on recording additional affiliations, and each institution noted will have an equally valid claim to the output for reporting purposes.

What do I do if my output is missing a Charles Sturt University byline?

If there is no Charles Sturt University byline on the output, we require an  author's declaration form to be completed indicating that the research leading to the publication was undertaken in your capacity as an affiliate of Charles Sturt University. You can enter multiple outputs onto the one form to save time.

Please note that a 'currently employed at' byline note (or similar) is insufficient proof that the research was actually undertaken while affiliated with Charles Sturt University, so you will need to complete an author's declaration if appropriate.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.9(2); 2019

Author misrepresentation of institutional affiliations: protocol for an exploratory case study

Vivienne c bachelet.

Facultad de Ciencias Médicas, Universidad de Santiago de Chile, Santiago, Chile

Francisco A Uribe

Ruben a díaz, alonso f vergara, fabiana bravo-córdova, víctor a carrasco, francisca j lizana, nicolás meza-ducaud, maría s navarrete, associated data, introduction.

University ranking systems and the publish-or-perish dictum, among other factors, are driving universities and researchers around the world to increase their research productivity. Authors frequently report multiple affiliations in published articles. It is not known if the reported institutional affiliations are real affiliations, which is when the universities have contributed substantially to the research conducted and to the published manuscript. This study aims to establish whether there is an empirical basis for author affiliation misrepresentation in authors with multiple institutional affiliations.

Methods and analysis

This individual secondary data exploratory analysis on Scopus-indexed articles for 2016 will search all authors who report multiple institutional affiliations in which at least one of the affiliations is to a Chilean university. We will consider that misrepresentation of an affiliation is more likely when it is not possible to verify objectively a link between the author and the mentioned institution through institutional websites. If we cannot corroborate the author affiliation, we will consider this a finding of potential misrepresentation of the affiliation. We will summarise results with descriptive statistics.

Ethics and dissemination

The study protocol was approved by the institutional ethics committee of Universidad de Santiago de Chile, Resolution No. 261, and dated January 15, 2018. Results will be submitted to the World Conference on Research Integrity, among other meetings on publication ethics and research integrity, and will be published in scientific, peer-reviewed journals.

Strengths and limitations of this study

• This study introduces the concept of misrepresentation of author institutional affiliation.
• To the best of our knowledge, this will be the first study to explore the prevalence of potential author institutional affiliation misrepresentation.
• Manual data extraction from the Scopus database may increase the risk of measurement error.
• Underestimation or overestimation of study results may occur from information bias.

The push to publish is a well-known and pervasive phenomenon in academia in many different countries. 1–5 The publish-or-perish dictum puts pressure both on academics and higher education institutions. 5–8 Academics strive to further their careers, and one of the indicators they are measured by is their research productivity and citations. 7 9 10 Similarly, universities are interested in bettering their position in the ranking systems, obtaining core and complementary funds and complying with local or international accreditation systems. 11 All of these performance measures rely to an extent on scientific publications—especially those included in the major databases, such as Web of Science or Scopus.

Authorship issues are a central concern in publication ethics 12 and research integrity. While much has been written about ghost authorship 1 2 13–19 and studies have been conducted on its prevalence, 20–25 little is known about authors who publish and report multiple institutional affiliations—either university or other—in the author byline. The American Psychological Association (APA) states that “the institutional affiliation identifies the location where the author or authors were when the research was conducted, which is usually an institution.” 26 It then goes on to recommend that a dual affiliation may be included only if ‘two institutions contributed substantial support to the study.’

Several factors may contribute to multiple affiliations. 27 Authors may seek access to resources, networks and infrastructure, on the positive side; or they may be interested in personal financial gain 28 as may be accrued from universities that pay honoraria to authors to include the university affiliation in the author byline, on the negative side. Accordingly, we could assume that there are ‘legitimate’ multiple affiliations—ie, those that comply with the APA guidelines—and ‘non-legitimate’ multiple affiliations, where at least one of the affiliations is not reflecting a real or substantial contribution by the institution to the study. We understand the latter to be a potential misrepresentation of an institutional affiliation, and this could entail a research integrity breach. However, neither the Committee on Publication Ethics, nor the International Committee of Medical Journal Editors has issued discussion documents or recommendations on the potential ethical implications of affiliation misrepresentation as could occur when the institutions reported have not contributed substantial support to the study.

We believe there may be cases of misrepresentation of affiliations in some articles whose authors report multiple institutional affiliations. Anecdotal verbal reports of misrepresentation of affiliations to Chilean universities have drawn our attention to this possibly emerging misconduct in research integrity. Based on our and other’s observations, we know that some authors reporting an affiliation to a Chilean university have received fees for the only purpose of adding on the institutional affiliation, while not having any real academic or research-based employment relationship with the university. The reported institutions of our observations have not contributed any support to the underlying research or the published manuscript.

Chile is a small country with a limited number of universities and stiff competition for prestige and student enrolment. According to the World Bank Country Profile , Chile is a high-income country with a lightly regulated market-based economy, and the private sector is the main provider of higher education. 29–31 In recent decades, there have been significant efforts to strengthen the quality accreditation process with the purpose of ensuring that minimum quality standards are met. 32 Universities are classified under three mutually exclusive categories: state-owned and run; private and traditional (founded before 1981); and private non-traditional (founded after 1981). 29 Profit is not allowed in the Chilean higher education system. 29 In 2016, there were 62 universities in Chile, 29 52 of which have at least one article published during 2016 indexed in Scopus. Of these 52 organisations, 16 belong to the state-owned-and-run category, nine are private traditional, and 27 are private non-traditional.

If we are to confirm the emerging problem of institutional misrepresentation of affiliations, the drivers behind it are the need for universities to rise in the international ranking systems for higher education, 11 30 the linked public funding that is attached to the publications that report university affiliations, 29–31 and the local quality certification processes. 33 In other words, we suspect that some institutions might be attempting to game the higher education ranking systems by spuriously pumping up their productivity, while at the same time receiving the benefits of state funding and higher student enrolment. The rise of several non-traditional universities in different rankings in recent years 34–38 has raised a warning and underpins the need for further research into self-reported institutional affiliations. The consequences of this potential misconduct are important since there would be a violation of public trust, misappropriation of public funds and a distortion of the indicators used by the higher education ranking houses.

The purpose of this exploratory study is to examine and verify affiliations of authors who report multiple affiliations with at least one belonging to a Chilean higher education institution.

Study design

The design is an individual secondary data exploratory study on Scopus-indexed articles during 2016.

Our primary aim is to establish the prevalence of author-reported affiliations to Chilean universities that might potentially misrepresent the true author affiliation based on whether they can be found through a simple Google search. A secondary aim of our study is to determine Open Researcher and Contributor Identification (ORCID) consistency regarding affiliations to Chilean institutions.

Data sources

We will retrieve all of the articles that have at least one author affiliated to a Chilean university in 2016 as registered by the Scopus database. Scopus is one of the main multidiscipline abstract and citation databases of peer-reviewed literature. It includes scientific journals, books and conference proceedings on research in the fields of science, technology, medicine, social sciences, and arts and humanities. We will use 2016 because this is the most recent year that reasonably includes all articles published during the year in Scopus-indexed scholarly journals.

Affiliations should reflect the contribution that the organisation is giving to the published article, so for this study, we will ascertain whether the reported affiliation is suspect of potential misrepresentation by doing a Google search of the institutional websites, that is, the fact-checking process for this study.

We will also determine the consistency of ORCID author identification database with the affiliation reported in the article. ORCID is a non-profit organisation that helps researchers to be uniquely identified and connected with their contributions and affiliations.

Data for covariables will be obtained from the following: areas of research from Scopus; university profiles from Chilean regulatory documents; and journal impact factor from the Journal Citation Report of Clarivate Analytics.

Search strategy

To retrieve the articles in Scopus, we developed a specific search strategy for each university, limited to 2016 and document-type ‘article’. Table 1 provides a masked example of the structure of our search strategy, where ‘ABCD’ can be substituted for the name of any Chilean university. The search strategy also includes a unique Scopus affiliation identifier, also masked in table 1 as ‘12345678’. The complete Scopus search strategy for all existing Chilean institutions in 2016 is available as supplementary material (available at https://doi.org/10.6084/m9.figshare.5825943.v1 ).

Example of search strategy used in Scopus to find articles with authors affiliated to Chilean universities

When running all the search strategies, we expect to find over 12 000 articles.

Eligibility

Authors may report single or multiple affiliations. We define ‘multiple affiliation’ as the reporting of more than one affiliation to organisations involved in research—universities, laboratories, commercial research companies, hospitals, non-profit organisations and so forth.

We will include in our study any author who reports in the article author byline an affiliation to a Chilean university, in articles of any field of study. Authors who report an affiliation to a Chilean university may be Chilean, based in Chile; a foreigner, based in Chile; a foreigner, based out of Chile and Chilean based out of Chile. We will not consider the author multiple affiliated if he or she reports different departments belonging to one institution.

Article selection

We will screen all the eligible records to find authors affiliated with Chilean universities, and we will determine whether they have single or multiple affiliations. Articles that include authors reporting multiple affiliations will be selected for the study.

Seven reviewers working in parallel will retrieve article titles with no duplicate screening of articles for eligibility. Then, we will extract the following data from each article: article title, journal in which the article was published, author’s name according to Scopus author details—thus preventing confusion between authors with similar names—, Chilean university affiliation as it appears in the article Scopus record and author affiliation details according to the Scopus author record.

Data extraction

For all authors affiliated to a Chilean institution, we will check if those affiliations can be substantiated or not. To do so, we will look into two sources: ORCID database and institutional websites ( figure 1 ). ORCID is a self-reported database, so the authors may not have updated their institutional affiliations, that is, we cannot be sure that the information provided by the authors is complete, updated, transparent or accurate. Likewise, institutional websites might not be updated or might not provide an accurate and complete list of affiliated faculties. However, institutional websites are not self-reported by authors, which helps to offset the potential bias in using ORCID.

Fact-checking process for potential misrepresentation of institutional affiliations.

We will search within the websites of the Chilean universities to find any mention of the authors with affiliations to them. To do this, we will begin by conducting a Google search using the Scopus disambiguated name. We will use the complete disambiguated name; ie, if Scopus reports only the last name and an initial, we will query the Scopus record for the author, and we will look for ‘other name formats’ to find a complete name that includes last name and first name. We will also add the name of the department or faculty that is being reported in the article to make the Google search more specific. We will set Google to search for results in Chile by default, that is, www.google.cl . We will use this search to verify whether the affiliation reported in the article appears corroborated by the results of the first two Google pages.

We will extract the ORCID id as reported by Scopus, and we will verify whether the author mentions the Chilean institution in his or her ORCID. We will conduct this verification for each unique author who reports an affiliation to a Chilean university in an article indexed in Scopus during 2016. The ORCID author record contains several sections, one of which is ‘employment’. We will use this section to verify whether the affiliation reported in the article is also reported in the ORCID author record. If Scopus reports no ORCID for the author, we will hand search the author in ORCID. To hand search in ORCID, we will use the author name as supplied by Scopus. We will screen the first ten records provided by ORCID to find an exact or very close match for the author name, at least to the first and last names. Since ORCID is not disambiguated but instead relies on the author-reported information, more than one author may have the same name. This is a limitation of the ORCID database. To overcome this limitation, we will apply reviewer judgement, discussion and consensus. If the article author affiliation appears in the ORCID record, we will register this information in the data extraction form.

Since our extraction will be done manually, we will introduce quality controls on the data extraction process by conducting crosschecking among the different reviewers. Each reviewer will manually check whether all of the articles resulting from the search strategy assigned to another reviewer were properly extracted into the database. Next, each reviewer will double check each field to detect errors that could arise during data extraction (eg, adding a blank space at the end of a name). After this procedure is completed, a sample will be obtained for each reviewer to measure error rate; if there is at least one error within the sample, the reviewer will have to go through the database again and correct any errors or omissions.

Statistical analysis

Since there is no prior estimation of the frequency of the event—ie, misrepresentation of author institutional affiliations—we will include the whole population of records available in the Scopus database for 2016, in which at least one author reports at least one affiliation to a Chilean university. Hence, no sampling will be done, and no hypothesis testing is appropriate for these data.

We will consider that potential misrepresentation of affiliation is more likely when it is not possible to objectively verify a link between the author and the mentioned institution. When an author reports the affiliation in ORCID, we will register this finding. A Google search will be conducted to verify whether the affiliation can be substantiated through institutional websites. If we cannot corroborate author affiliation through ORCID and institutional websites, we will consider this to be a finding for potential misrepresentation of the affiliation.

We expect the observed frequency of events to be low and will be reported as a rate. We do not expect to have missing data.

The statistical analysis will be mainly descriptive; the primary objective is to determine the prevalence of potential author institutional affiliation misrepresentation. Exploratory attempts will be made to associate the observed distribution of cases with specific research areas of knowledge (agriculture, energy, health and so on), university profiles (private versus public, traditional versus more recently founded universities and so on), and journal impact factor. We will summarise the findings with descriptive statistics such as means, medians, graphs and tables. We will report the consistency between ORCID self-reporting and corroboration with institutional websites as a proportion. We will report results as per the flowchart shown in figure 2 .

Flowchart of authors reporting Chilean institutional affiliations in Scopus. ORCID, Open Researcher and Contributor Identification.

Due to the possible contentious nature of the matter of this study, we submitted our study protocol to ethical oversight and approval.

Results will be submitted to the World Conference on Research Integrity, among other meetings on publication ethics and research integrity, and will be published in scientific, peer-reviewed journals. The dataset will be openly available at Figshare.

Patient and public involvement

Neither patients nor public were involved in the development of this study protocol.

We are reporting the protocol for an exploratory study to be conducted in Chile on the potential problem of author affiliation misrepresentation. We expect that the findings of this study might apply to other countries as well. For this reason, we have developed a protocol that could easily be replicated by other investigators. If our working hypothesis is confirmed, this will be the first study to introduce the concept of potential misrepresentation of institutional affiliation and report on its prevalence. Since ORCID is not mandatory, it is also interesting to assess the penetration of its use among authors reporting an affiliation to a Chilean university, as well as to gauge the consistency between ORCID and institutional websites. Notwithstanding, we are aware that more information is needed on the individual authors to derive any conclusions on potential author misrepresentation of institutional affiliations than what is being collected for this study according to our proposed methods. Certainly no causal relationships can be expected from this study.

Chile has a market-based economy, with a higher education system similar to other countries. In the 2007–2016 period, student enrolment grew at an average annual rate of 5.2%, from 748 405 to 1 178 437. 30 Universities obtain their funding mainly from enrolment, but they also receive core funding from the state. Core funding in Chile is linked to published papers included in Scopus. 30 Similarly, research productivity is a key indicator heavily used by most ranking houses. 11 39 Thus, it would not come as a surprise that universities would attempt to recruit highly published and highly cited academics in order to rise in international rankings, a phenomenon that has been reported elsewhere as well. 40

Few studies have been conducted on the phenomenon of multiple affiliations. One of them showed that multiple affiliations have doubled in recent years in Germany, Japan and the UK. 27 This may be due to greater international collaboration and globalisation due to modern communications technologies, as well as to increased mobility in academia. Affiliations to different organisations can be construed as facilitating knowledge exchange, without this entailing that there is a misrepresentation of the affiliation. Universities should pay authors for their work, but there may also be secondary appointments that include visiting positions, courtesy appointments or emeritus status, among others. These secondary or honorary appointments may enable and further facilitate continued collaboration between researchers, with no additional commitment for space or resources on the part of the institution. Some institutions have complete descriptions of the privileges and responsibilities of these secondary appointments. Since there are no consensus-based guidelines on what defines an author affiliation and, to our best knowledge, there is none beyond the APA definition, we believe there is room for manipulation of this key information during the process of article submission for unethical ends. In effect, further research is needed to determine the true underlying causes for the increase of multiple affiliations.

Institutional author affiliation misrepresentation, if ascertained, is a complex problem with many potential causes, some of which may be context specific, and international drivers such as ranking and academic compensation systems may explain others. In our study, we have strived to design a protocol that accounts for the most commonly identified uses for author affiliations and that are present in all countries with robust university systems.

Strengths and limitations

We have thoughtfully considered the difficulties in tying an investigator’s name to a supporting institution, especially considering that many times universities are not prompt at disclosing on their websites who is affiliated to them. Nonetheless, we believe this is an institutional mandate that world-class universities, that is, those that seek to appear in the ranking systems, must comply with. Accordingly, the study group chose an approach that we believe is one of the strengths of this study, which is to corroborate author institutional linkage with third-party entities—ORCID and institutional websites—thus minimising the risk of information bias and methodological variability.

Robustness of our data depends greatly on the soundness of the individual Scopus author and article records, which are not self-reported. This is a strength of the study, because we are using a standardised and internationally recognised data source for scientific productivity, but it is also a limitation, as there could be potential errors in the Scopus dataset that we would not be able to correct for. 41 Nevertheless, Scopus disambiguates author names in the Scopus Author Profile, which is automatically created by Scopus using a sophisticated algorithmic profiling that Scopus itself admits is not 100% accurate. 42 Using the data extracted, we will be able to explore associations with journals (including journal disciplinary category and impact factor), author countries of origin and category of university that the suspect affiliation is reporting, among others. This will provide us with a full characterisation and scope of author multiple affiliations that include affiliations to Chilean universities. Exploring the whole dataset of authors reporting an affiliation to a Chilean institution in a given year instead of resorting to a sampling strategy is, in our view, a strength of this study, but the manual extraction of data from the Scopus website is a limitation. This could be offset by an electronic export of the Scopus database thus helping reduce measurement error.

Another strength of this study is that we are using internationally established standards to corroborate institutional affiliations, such as ORCID and Google, present in most countries. This study will also help to provide numbers for the penetration of ORCID with authors reporting an affiliation to a Chilean higher education institution. While Scopus author records are not self-reported, ORCID is self-reported, making the corroboration of institutional affiliation through ORCID susceptible to information bias. Conversely, institutional websites are not self-reported but may not be updated. Combining these multiple checkpoints should make our conclusions more robust.

This study has many limitations that cannot be corrected for by method. For example, we will not able to corroborate whether an author is using a single affiliation that is misrepresented versus multiple affiliations that might or might not be misrepresented. The former could be the case if an author publishes an article on behalf of an institution while not being a member of faculty or having any real linkage to the university.

We hope that our study results will contribute to raise awareness and guide key stakeholders in developing standards for reporting institutional affiliations. Affiliations should truly reflect the support and contributions provided to the research and the publication of the study results by the universities mentioned in the author byline. If we validate the existence of author and institutional misconduct in the case of Chile and applying the methodology that this study will pilot, we are interested in conducting a larger more comprehensive study that would include a panel of countries to represent both emerging economies and developed countries.

Supplementary Material

Acknowledgments.

The authors wish to thank Eva Madrid (Universidad de Valparaíso, Chile), Adrian Ziderman (Bar-Ilan University, Israel) and Miguel Roig (St. John’s University, United States) for providing helpful ideas, guidance and comments.

Contributors: Study idea (VCB); conceptualisation (VCB, FAU, RAD, AFV, MSN, FBC, VAC, FJL, NMD); methodology (VCB, FAU, RAD, AFV, MSN); original draft preparation (VCB, FAU); review and editing (VCB, FAU, RAD, AFV, MSN, FBC, VAC, FJL, NMD); supervision (VCB, FAU); project administration (VCB).

Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests: All authors are affiliated to a state university and this might be construed as a potential bias regarding the research protocol reported. The lead author (VCB) is member of the Committee on Publication Ethics council.

Ethics approval: The institutional ethics committee of Universidad de Santiago de Chile, Resolution No. 261, dated 15 January 2018, approved the study protocol.

Provenance and peer review: Not commissioned; externally peer reviewed.

Patient consent for publication: Not required.

Our approach

• Responsibility
• Infrastructure
• Try Meta AI

RECOMMENDED READS

• 5 Steps to Getting Started with Llama 2
• The Llama Ecosystem: Past, Present, and Future
• Introducing Code Llama, a state-of-the-art large language model for coding
• Meta and Microsoft Introduce the Next Generation of Llama
• Today, we’re introducing Meta Llama 3, the next generation of our state-of-the-art open source large language model.
• Llama 3 models will soon be available on AWS, Databricks, Google Cloud, Hugging Face, Kaggle, IBM WatsonX, Microsoft Azure, NVIDIA NIM, and Snowflake, and with support from hardware platforms offered by AMD, AWS, Dell, Intel, NVIDIA, and Qualcomm.
• We’re dedicated to developing Llama 3 in a responsible way, and we’re offering various resources to help others use it responsibly as well. This includes introducing new trust and safety tools with Llama Guard 2, Code Shield, and CyberSec Eval 2.
• In the coming months, we expect to introduce new capabilities, longer context windows, additional model sizes, and enhanced performance, and we’ll share the Llama 3 research paper.
• Meta AI, built with Llama 3 technology, is now one of the world’s leading AI assistants that can boost your intelligence and lighten your load—helping you learn, get things done, create content, and connect to make the most out of every moment. You can try Meta AI here .

Today, we’re excited to share the first two models of the next generation of Llama, Meta Llama 3, available for broad use. This release features pretrained and instruction-fine-tuned language models with 8B and 70B parameters that can support a broad range of use cases. This next generation of Llama demonstrates state-of-the-art performance on a wide range of industry benchmarks and offers new capabilities, including improved reasoning. We believe these are the best open source models of their class, period. In support of our longstanding open approach, we’re putting Llama 3 in the hands of the community. We want to kickstart the next wave of innovation in AI across the stack—from applications to developer tools to evals to inference optimizations and more. We can’t wait to see what you build and look forward to your feedback.

Our goals for Llama 3

With Llama 3, we set out to build the best open models that are on par with the best proprietary models available today. We wanted to address developer feedback to increase the overall helpfulness of Llama 3 and are doing so while continuing to play a leading role on responsible use and deployment of LLMs. We are embracing the open source ethos of releasing early and often to enable the community to get access to these models while they are still in development. The text-based models we are releasing today are the first in the Llama 3 collection of models. Our goal in the near future is to make Llama 3 multilingual and multimodal, have longer context, and continue to improve overall performance across core LLM capabilities such as reasoning and coding.

State-of-the-art performance

Our new 8B and 70B parameter Llama 3 models are a major leap over Llama 2 and establish a new state-of-the-art for LLM models at those scales. Thanks to improvements in pretraining and post-training, our pretrained and instruction-fine-tuned models are the best models existing today at the 8B and 70B parameter scale. Improvements in our post-training procedures substantially reduced false refusal rates, improved alignment, and increased diversity in model responses. We also saw greatly improved capabilities like reasoning, code generation, and instruction following making Llama 3 more steerable.

*Please see evaluation details for setting and parameters with which these evaluations are calculated.

In the development of Llama 3, we looked at model performance on standard benchmarks and also sought to optimize for performance for real-world scenarios. To this end, we developed a new high-quality human evaluation set. This evaluation set contains 1,800 prompts that cover 12 key use cases: asking for advice, brainstorming, classification, closed question answering, coding, creative writing, extraction, inhabiting a character/persona, open question answering, reasoning, rewriting, and summarization. To prevent accidental overfitting of our models on this evaluation set, even our own modeling teams do not have access to it. The chart below shows aggregated results of our human evaluations across of these categories and prompts against Claude Sonnet, Mistral Medium, and GPT-3.5.

Preference rankings by human annotators based on this evaluation set highlight the strong performance of our 70B instruction-following model compared to competing models of comparable size in real-world scenarios.

Our pretrained model also establishes a new state-of-the-art for LLM models at those scales.

To develop a great language model, we believe it’s important to innovate, scale, and optimize for simplicity. We adopted this design philosophy throughout the Llama 3 project with a focus on four key ingredients: the model architecture, the pretraining data, scaling up pretraining, and instruction fine-tuning.

Model architecture

In line with our design philosophy, we opted for a relatively standard decoder-only transformer architecture in Llama 3. Compared to Llama 2, we made several key improvements. Llama 3 uses a tokenizer with a vocabulary of 128K tokens that encodes language much more efficiently, which leads to substantially improved model performance. To improve the inference efficiency of Llama 3 models, we’ve adopted grouped query attention (GQA) across both the 8B and 70B sizes. We trained the models on sequences of 8,192 tokens, using a mask to ensure self-attention does not cross document boundaries.

Training data

To train the best language model, the curation of a large, high-quality training dataset is paramount. In line with our design principles, we invested heavily in pretraining data. Llama 3 is pretrained on over 15T tokens that were all collected from publicly available sources. Our training dataset is seven times larger than that used for Llama 2, and it includes four times more code. To prepare for upcoming multilingual use cases, over 5% of the Llama 3 pretraining dataset consists of high-quality non-English data that covers over 30 languages. However, we do not expect the same level of performance in these languages as in English.

To ensure Llama 3 is trained on data of the highest quality, we developed a series of data-filtering pipelines. These pipelines include using heuristic filters, NSFW filters, semantic deduplication approaches, and text classifiers to predict data quality. We found that previous generations of Llama are surprisingly good at identifying high-quality data, hence we used Llama 2 to generate the training data for the text-quality classifiers that are powering Llama 3.

We also performed extensive experiments to evaluate the best ways of mixing data from different sources in our final pretraining dataset. These experiments enabled us to select a data mix that ensures that Llama 3 performs well across use cases including trivia questions, STEM, coding, historical knowledge, etc.

Scaling up pretraining

To effectively leverage our pretraining data in Llama 3 models, we put substantial effort into scaling up pretraining. Specifically, we have developed a series of detailed scaling laws for downstream benchmark evaluations. These scaling laws enable us to select an optimal data mix and to make informed decisions on how to best use our training compute. Importantly, scaling laws allow us to predict the performance of our largest models on key tasks (for example, code generation as evaluated on the HumanEval benchmark—see above) before we actually train the models. This helps us ensure strong performance of our final models across a variety of use cases and capabilities.

We made several new observations on scaling behavior during the development of Llama 3. For example, while the Chinchilla-optimal amount of training compute for an 8B parameter model corresponds to ~200B tokens, we found that model performance continues to improve even after the model is trained on two orders of magnitude more data. Both our 8B and 70B parameter models continued to improve log-linearly after we trained them on up to 15T tokens. Larger models can match the performance of these smaller models with less training compute, but smaller models are generally preferred because they are much more efficient during inference.

To train our largest Llama 3 models, we combined three types of parallelization: data parallelization, model parallelization, and pipeline parallelization. Our most efficient implementation achieves a compute utilization of over 400 TFLOPS per GPU when trained on 16K GPUs simultaneously. We performed training runs on two custom-built 24K GPU clusters . To maximize GPU uptime, we developed an advanced new training stack that automates error detection, handling, and maintenance. We also greatly improved our hardware reliability and detection mechanisms for silent data corruption, and we developed new scalable storage systems that reduce overheads of checkpointing and rollback. Those improvements resulted in an overall effective training time of more than 95%. Combined, these improvements increased the efficiency of Llama 3 training by ~three times compared to Llama 2.

Instruction fine-tuning

To fully unlock the potential of our pretrained models in chat use cases, we innovated on our approach to instruction-tuning as well. Our approach to post-training is a combination of supervised fine-tuning (SFT), rejection sampling, proximal policy optimization (PPO), and direct preference optimization (DPO). The quality of the prompts that are used in SFT and the preference rankings that are used in PPO and DPO has an outsized influence on the performance of aligned models. Some of our biggest improvements in model quality came from carefully curating this data and performing multiple rounds of quality assurance on annotations provided by human annotators.

Learning from preference rankings via PPO and DPO also greatly improved the performance of Llama 3 on reasoning and coding tasks. We found that if you ask a model a reasoning question that it struggles to answer, the model will sometimes produce the right reasoning trace: The model knows how to produce the right answer, but it does not know how to select it. Training on preference rankings enables the model to learn how to select it.

Building with Llama 3

Our vision is to enable developers to customize Llama 3 to support relevant use cases and to make it easier to adopt best practices and improve the open ecosystem. With this release, we’re providing new trust and safety tools including updated components with both Llama Guard 2 and Cybersec Eval 2, and the introduction of Code Shield—an inference time guardrail for filtering insecure code produced by LLMs.

We’ve also co-developed Llama 3 with torchtune , the new PyTorch-native library for easily authoring, fine-tuning, and experimenting with LLMs. torchtune provides memory efficient and hackable training recipes written entirely in PyTorch. The library is integrated with popular platforms such as Hugging Face, Weights & Biases, and EleutherAI and even supports Executorch for enabling efficient inference to be run on a wide variety of mobile and edge devices. For everything from prompt engineering to using Llama 3 with LangChain we have a comprehensive getting started guide and takes you from downloading Llama 3 all the way to deployment at scale within your generative AI application.

A system-level approach to responsibility

We have designed Llama 3 models to be maximally helpful while ensuring an industry leading approach to responsibly deploying them. To achieve this, we have adopted a new, system-level approach to the responsible development and deployment of Llama. We envision Llama models as part of a broader system that puts the developer in the driver’s seat. Llama models will serve as a foundational piece of a system that developers design with their unique end goals in mind.

Instruction fine-tuning also plays a major role in ensuring the safety of our models. Our instruction-fine-tuned models have been red-teamed (tested) for safety through internal and external efforts. ​​Our red teaming approach leverages human experts and automation methods to generate adversarial prompts that try to elicit problematic responses. For instance, we apply comprehensive testing to assess risks of misuse related to Chemical, Biological, Cyber Security, and other risk areas. All of these efforts are iterative and used to inform safety fine-tuning of the models being released. You can read more about our efforts in the model card .

Llama Guard models are meant to be a foundation for prompt and response safety and can easily be fine-tuned to create a new taxonomy depending on application needs. As a starting point, the new Llama Guard 2 uses the recently announced MLCommons taxonomy, in an effort to support the emergence of industry standards in this important area. Additionally, CyberSecEval 2 expands on its predecessor by adding measures of an LLM’s propensity to allow for abuse of its code interpreter, offensive cybersecurity capabilities, and susceptibility to prompt injection attacks (learn more in our technical paper ). Finally, we’re introducing Code Shield which adds support for inference-time filtering of insecure code produced by LLMs. This offers mitigation of risks around insecure code suggestions, code interpreter abuse prevention, and secure command execution.

With the speed at which the generative AI space is moving, we believe an open approach is an important way to bring the ecosystem together and mitigate these potential harms. As part of that, we’re updating our Responsible Use Guide (RUG) that provides a comprehensive guide to responsible development with LLMs. As we outlined in the RUG, we recommend that all inputs and outputs be checked and filtered in accordance with content guidelines appropriate to the application. Additionally, many cloud service providers offer content moderation APIs and other tools for responsible deployment, and we encourage developers to also consider using these options.

Deploying Llama 3 at scale

Llama 3 will soon be available on all major platforms including cloud providers, model API providers, and much more. Llama 3 will be everywhere .

Our benchmarks show the tokenizer offers improved token efficiency, yielding up to 15% fewer tokens compared to Llama 2. Also, Group Query Attention (GQA) now has been added to Llama 3 8B as well. As a result, we observed that despite the model having 1B more parameters compared to Llama 2 7B, the improved tokenizer efficiency and GQA contribute to maintaining the inference efficiency on par with Llama 2 7B.

For examples of how to leverage all of these capabilities, check out Llama Recipes which contains all of our open source code that can be leveraged for everything from fine-tuning to deployment to model evaluation.

What’s next for Llama 3?

The Llama 3 8B and 70B models mark the beginning of what we plan to release for Llama 3. And there’s a lot more to come.

Our largest models are over 400B parameters and, while these models are still training, our team is excited about how they’re trending. Over the coming months, we’ll release multiple models with new capabilities including multimodality, the ability to converse in multiple languages, a much longer context window, and stronger overall capabilities. We will also publish a detailed research paper once we are done training Llama 3.

To give you a sneak preview for where these models are today as they continue training, we thought we could share some snapshots of how our largest LLM model is trending. Please note that this data is based on an early checkpoint of Llama 3 that is still training and these capabilities are not supported as part of the models released today.

We’re committed to the continued growth and development of an open AI ecosystem for releasing our models responsibly. We have long believed that openness leads to better, safer products, faster innovation, and a healthier overall market. This is good for Meta, and it is good for society. We’re taking a community-first approach with Llama 3, and starting today, these models are available on the leading cloud, hosting, and hardware platforms with many more to come.

Try Meta Llama 3 today

We’ve integrated our latest models into Meta AI, which we believe is the world’s leading AI assistant. It’s now built with Llama 3 technology and it’s available in more countries across our apps.

You can use Meta AI on Facebook, Instagram, WhatsApp, Messenger, and the web to get things done, learn, create, and connect with the things that matter to you. You can read more about the Meta AI experience here .

Visit the Llama 3 website to download the models and reference the Getting Started Guide for the latest list of all available platforms.

You’ll also soon be able to test multimodal Meta AI on our Ray-Ban Meta smart glasses.

As always, we look forward to seeing all the amazing products and experiences you will build with Meta Llama 3.

Our latest updates delivered to your inbox

Subscribe to our newsletter to keep up with Meta AI news, events, research breakthroughs, and more.

Join us in the pursuit of what’s possible with AI.

Product experiences

Foundational models

Latest news

Meta © 2024

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

Changing Partisan Coalitions in a Politically Divided Nation

Party identification among registered voters, 1994-2023, table of contents.

• What this report tells us – and what it doesn’t
• Partisans and partisan leaners in the U.S. electorate
• Party identification and ideology
• Education and partisanship
• Education, race and partisanship
• Partisanship by race and gender
• Partisanship across educational and gender groups by race and ethnicity
• Gender and partisanship
• Parents are more Republican than voters without children
• Partisanship among men and women within age groups
• Race, age and partisanship
• The partisanship of generational cohorts
• Religion, race and ethnicity, and partisanship
• Party identification among atheists, agnostics and ‘nothing in particular’
• Partisanship and religious service attendance
• Partisanship by income groups
• The relationship between income and partisanship differs by education
• Union members remain more Democratic than Republican
• Homeowners are more Republican than renters
• Partisanship of military veterans
• Demographic differences in partisanship by community type
• Race and ethnicity
• Age and the U.S. electorate
• Education by race and ethnicity
• Religious affiliation
• Ideological composition of voters
• Acknowledgments
• Overview of survey methodologies
• The 2023 American Trends Panel profile survey methodology
• Measuring party identification across survey modes
• Adjusting telephone survey trends
• Appendix B: Religious category definitions
• Appendix C: Age cohort definitions

Pew Research Center conducted this analysis to explore partisan identification among U.S. registered voters across major demographic groups and how voters’ partisan affiliation has shifted over time. It also explores the changing composition of voters overall and the partisan coalitions.

For this analysis, we used annual totals of data from Pew Research Center telephone surveys (1994-2018) and online surveys (2019-2023) among registered voters. All telephone survey data was adjusted to account for differences in how people respond to surveys on the telephone compared with online surveys (refer to Appendix A for details).

All online survey data is from the Center’s nationally representative American Trends Panel . The surveys were conducted in both English and Spanish. Each survey is weighted to be representative of the U.S. adult population by gender, age, education, race and ethnicity and other categories. Read more about the ATP’s methodology , as well as how Pew Research Center measures many of the demographic categories used in this report .

The contours of the 2024 political landscape are the result of long-standing patterns of partisanship, combined with the profound demographic changes that have reshaped the United States over the past three decades.

Many of the factors long associated with voters’ partisanship remain firmly in place. For decades, gender, race and ethnicity, and religious affiliation have been important dividing lines in politics. This continues to be the case today.

Yet there also have been profound changes – in some cases as a result of demographic change, in others because of dramatic shifts in the partisan allegiances of key groups.

The combined effects of change and continuity have left the country’s two major parties at virtual parity: About half of registered voters (49%) identify as Democrats or lean toward the Democratic Party, while 48% identify as Republicans or lean Republican.

In recent decades, neither party has had a sizable advantage, but the Democratic Party has lost the edge it maintained from 2017 to 2021. (Explore this further in Chapter 1 . )

Pew Research Center’s comprehensive analysis of party identification among registered voters – based on hundreds of thousands of interviews conducted over the past three decades – tracks the changes in the country and the parties since 1994. Among the major findings:

The partisan coalitions are increasingly different. Both parties are more racially and ethnically diverse than in the past. However, this has had a far greater impact on the composition of the Democratic Party than the Republican Party.

The share of voters who are Hispanic has roughly tripled since the mid-1990s; the share who are Asian has increased sixfold over the same period. Today, 44% of Democratic and Democratic-leaning voters are Hispanic, Black, Asian, another race or multiracial, compared with 20% of Republicans and Republican leaners. However, the Democratic Party’s advantages among Black and Hispanic voters, in particular, have narrowed somewhat in recent years. (Explore this further in Chapter 8 .)

Education and partisanship: The share of voters with a four-year bachelor’s degree keeps increasing, reaching 40% in 2023. And the gap in partisanship between voters with and without a college degree continues to grow, especially among White voters. More than six-in-ten White voters who do not have a four-year degree (63%) associate with the Republican Party, which is up substantially over the past 15 years. White college graduates are closely divided; this was not the case in the 1990s and early 2000s, when they mostly aligned with the GOP. (Explore this further in Chapter 2 .)

Beyond the gender gap: By a modest margin, women voters continue to align with the Democratic Party (by 51% to 44%), while nearly the reverse is true among men (52% align with the Republican Party, 46% with the Democratic Party). The gender gap is about as wide among married men and women. The gap is wider among men and women who have never married; while both groups are majority Democratic, 37% of never-married men identify as Republicans or lean toward the GOP, compared with 24% of never-married women. (Explore this further in Chapter 3 .)

A divide between old and young: Today, each younger age cohort is somewhat more Democratic-oriented than the one before it. The youngest voters (those ages 18 to 24) align with the Democrats by nearly two-to-one (66% to 34% Republican or lean GOP); majorities of older voters (those in their mid-60s and older) identify as Republicans or lean Republican. While there have been wide age divides in American politics over the last two decades, this wasn’t always the case; in the 1990s there were only very modest age differences in partisanship. (Explore this further in Chapter 4 .)

Education and family income: Voters without a college degree differ substantially by income in their party affiliation. Those with middle, upper-middle and upper family incomes tend to align with the GOP. A majority with lower and lower-middle incomes identify as Democrats or lean Democratic. There are no meaningful differences in partisanship among voters with at least a four-year bachelor’s degree; across income categories, majorities of college graduate voters align with the Democratic Party. (Explore this further in Chapter 6 .)

Rural voters move toward the GOP, while the suburbs remain divided: In 2008, when Barack Obama sought his first term as president, voters in rural counties were evenly split in their partisan loyalties. Today, Republicans hold a 25 percentage point advantage among rural residents (60% to 35%). There has been less change among voters in urban counties, who are mostly Democratic by a nearly identical margin (60% to 37%). The suburbs – perennially a political battleground – remain about evenly divided. (Explore this further in Chapter 7 . )

Growing differences among religious groups: Mirroring movement in the population overall, the share of voters who are religiously unaffiliated has grown dramatically over the past 15 years. These voters, who have long aligned with the Democratic Party, have become even more Democratic over time: Today 70% identify as Democrats or lean Democratic. In contrast, Republicans have made gains among several groups of religiously affiliated voters, particularly White Catholics and White evangelical Protestants. White evangelical Protestants now align with the Republican Party by about a 70-point margin (85% to 14%). (Explore this further in Chapter 5 .)

In most cases, the partisan allegiances of voters do not change a great deal from year to year. Yet as this study shows, the long-term shifts in party identification are substantial and say a great deal about how the country – and its political parties – have changed since the 1990s.

The steadily growing alignment between demographics and partisanship reveals an important aspect of steadily growing partisan polarization. Republicans and Democrats do not just hold different beliefs and opinions about major issues , they are much more different racially, ethnically, geographically and in educational attainment than they used to be.

Yet over this period, there have been only modest shifts in overall partisan identification. Voters remain evenly divided, even as the two parties have grown further apart. The continuing close division in partisan identification among voters is consistent with the relatively narrow margins in the popular votes in most national elections over the past three decades.

Partisan identification provides a broad portrait of voters’ affinities and loyalties. But while it is indicative of voters’ preferences, it does not perfectly predict how people intend to vote in elections, or whether they will vote. In the coming months, Pew Research Center will release reports analyzing voters’ preferences in the presidential election, their engagement with the election and the factors behind candidate support.

Next year, we will release a detailed study of the 2024 election, based on validated voters from the Center’s American Trends Panel. It will examine the demographic composition and vote choices of the 2024 electorate and will provide comparisons to the 2020 and 2016 validated voter studies.

The partisan identification study is based on annual totals from surveys conducted on the Center’s American Trends Panel from 2019 to 2023 and telephone surveys conducted from 1994 to 2018. The survey data was adjusted to account for differences in how the surveys were conducted. For more information, refer to Appendix A .

Previous Pew Research Center analyses of voters’ party identification relied on telephone survey data. This report, for the first time, combines data collected in telephone surveys with data from online surveys conducted on the Center’s nationally representative American Trends Panel.

Directly comparing answers from online and telephone surveys is complex because there are differences in how questions are asked of respondents and in how respondents answer those questions. Together these differences are known as “mode effects.”

As a result of mode effects, it was necessary to adjust telephone trends for leaned party identification in order to allow for direct comparisons over time.

In this report, telephone survey data from 1994 to 2018 is adjusted to align it with online survey responses. In 2014, Pew Research Center randomly assigned respondents to answer a survey by telephone or online. The party identification data from this survey was used to calculate an adjustment for differences between survey mode, which is applied to all telephone survey data in this report.

Please refer to Appendix A for more details.

Sign up for The Briefing

Weekly updates on the world of news & information

• Education & Politics
• Election 2024
• Gender & Politics
• Political Parties
• Race, Ethnicity & Politics
• Religion & Politics
• Rural, Urban and Suburban Communities
• Voter Demographics

What’s It Like To Be a Teacher in America Today?

Republican gains in 2022 midterms driven mostly by turnout advantage, more americans disapprove than approve of colleges considering race, ethnicity in admissions decisions, partisan divides over k-12 education in 8 charts, school district mission statements highlight a partisan divide over diversity, equity and inclusion in k-12 education, most popular, report materials.

• Party Identification Detailed Tables, 1994-2023

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Age & Generations
• Coronavirus (COVID-19)
• Economy & Work
• Family & Relationships
• Gender & LGBTQ
• Immigration & Migration
• International Affairs
• Internet & Technology
• Methodological Research
• News Habits & Media
• Non-U.S. Governments
• Other Topics
• Politics & Policy
• Race & Ethnicity
• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

Copyright 2024 Pew Research Center

Terms & Conditions

Privacy Policy

Cookie Settings

Reprints, Permissions & Use Policy