Author response:
The following is the authors’ response to the original reviews.
Public Reviews:
Reviewer #1 (Public review)::
Summary:
The work used open peer reviews and followed them through a succession of reviews and author revisions. It assessed whether a reviewer had requested the author include additional citations and references to the reviewers' work. It then assessed whether the author had followed these suggestions and what the probability of acceptance was based on the authors decision.
Strengths and weaknesses:
The work's strengths are the in-depth and thorough statistical analysis it contains and the very large dataset it uses. The methods are robust and reported in detail. However, this is also a weakness of the work. Such thorough analysis makes it very hard to read! It's a very interesting paper with some excellent and thought provoking references but it needs to be careful not to overstate the results and improve the readability so it can be disseminated widely. It should also discuss more alternative explanations for the findings and, where possible, dismiss them.
I have toned down the language including a more neutral title. To help focus on the main results, I have moved four paragraphs from the methods to the supplement. These are the sample size, the two sensitivity analyses on including co-reviewers and confounding by reviewers’ characteristics, and the analysis examining potential bias for the reviewers with no OpenAlex record.
Reviewer #2 (Public review):
Summary:
This article examines reviewer coercion in the form of requesting citations to the reviewer's own work as a possible trade for acceptance and shows that, under certain conditions, this happens.
Strengths:
The methods are well done and the results support the conclusions that some reviewers "request" self-citations and may be making acceptance decisions based on whether an author fulfills that request.
Weaknesses:
The author needs to be more clear on the fact that, in some instances, requests for selfcitations by reviewers is important and valuable.
This is a key point. I have included a new text analysis to examine this issue and have addressed this in the updated discussion.
Reviewer #3 (Public review):
Summary:
In this article, Barnett examines a pressing question regarding citing behavior of authors during the peer review process. In particular, the author studies the interaction between reviewers and authors, focusing on the odds of acceptance, and how this may be affected by whether or not the authors cited the reviewers' prior work, whether the reviewer requested such citations be added, and whether the authors complied/how that affected the reviewer decision-making.
Strengths:
The author uses a clever analytical design, examining four journals that use the same open peer review system, in which the identities of the authors and reviewers are both available and linkable to structured data. Categorical information about the approval is also available as structured data. This design allows a large scale investigation of this question.
Weaknesses:
My concerns pertain to the interpretability of the data as presented and the overly terse writing style.
Regarding interpretability, it is often unclear what subset of the data are being used both in the prose and figures. For example, the descriptive statistics show many more Version 1 articles than Version 2+. How are the data subset among the different possible methods?
I have now included the number of articles and reviews in the legends of each plot. There are more version 1 articles because some are “approved” at this stage and hence a second version is never submitted (I’ve now specifically mentioned this in the discussion).
Likewise, the methods indicate that a matching procedure was used comparing two reviewers for the same manuscript in order to control for potential confounds. However, the number of reviews is less than double the number of Version 1 articles, making it unclear which data were used in the final analysis. The methods also state that data were stratified by version. This raises a question about which articles/reviews were included in each of the analyses. I suggest spending more space describing how the data are subset and stratified. This should include any conditional subsetting as in the analysis on the 441 reviews where the reviewer was not cited in Version 1 but requested a citation for Version 2. Each of the figures and tables, as well as statistics provided in the text should provide this information, which would make this paper much more accessible to the reader.
[Note from editor: Please see "Editorial feedback" for more on this]
The numbers are now given in every figure legend, and show the larger sample size for the first versions.
The analysis of the 441 reviews was an unplanned analysis that is separate to the planned models. The sample size is much smaller than the main models due to the multiple conditions applied to the reviewers: i) reviewed both versions, ii) not cited in first version, iii) requested a self-citation in their first review.
Finally, I would caution against imputing motivations to the reviewers, despite the important findings provided here. This is because the data as presented suggest a more nuanced interpretation is warranted. First, the author observes similar patterns of accept/reject decisions whether the suggested citation is a citation to the reviewer or not (Figs 3 and 4). Second, much of the observed reviewer behavior disappears or has much lower effect sizes depending on whether "Accept with Reservations" is considered an Accept or a Reject. This is acknowledged in the results text, but largely left out of the discussion. The conditional analysis on the 441 reviews mentioned above does support a more cautious version of the conclusion drawn here, especially when considered alongside the specific comments left by reviewers that were mentioned in the results and information in Table S.3. However, I recommend toning the language down to match the strength of the data.
I have used more cautious language throughout, including a new title. The new text analysis presented in the updated version also supports a more cautious approach.
Reviewer #4 (Public review):
Summary:
This work investigates whether a citation to a referee made by a paper is associated with a more positive evaluation by that referee for that paper. It provides evidence supporting this hypothesis. The work also investigates the role of self citations by referees where the referee would ask authors to cite the referee's paper.
Strengths:
This is an important problem: referees for scientific papers must provide their impartial opinions rooted in core scientific principles. Any undue influence due to the role of citations breaks this requirement. This work studies the possible presence and extent of this.
Barring a few issues discussed below, the methods are solid and well done. The work uses a matched pair design which controls for article-level confounding and further investigates robustness to other potential confounds.
It is surprising that even in these investigated journals where referee names are public, there is prevalence of such citation-related behaviors.
Weaknesses:
Some overall claims are questionable:
"Reviewers who were cited were more likely to approve the article, but only after version 1" It also appears that referees who were cited were less likely to approve the article in version 1. This null or slightly negative effect undermines the broad claim of citations swaying referees. The paper highlights only the positive results while not including the absence (and even reversal) of the effect in version 1 in its narrative.
The reversed effect for version 1 is interesting, but the adjusted 99.4% confidence interval includes 1 and hence it’s hard to be confident that this is genuinely in the reverse direction. However, it is certainly far from the strongly positive association for versions 2+.
"To the best of our knowledge, this is the first analysis to use a matched design when examining reviewer citations" Does not appear to be a valid claim based on the literature reference [18]
This previous paper used a matched design but then did not used a matched analysis. Hence, I’ve changed the text in my paper to “first analysis to use a matched design and analysis”. This may seem a minor claim of novelty, but not using a matched analysis for matched data could discard much of the benefits of the matching.
It will be useful to have a control group in the analysis associated to Figure 5 where the control group comprises matched reviews that did not ask for a self citation. This will help demarcate words associated with approval under self citation (as compared to when there is no self citation). The current narrative appears to suggest an association of the use of these words with self citations but without any control.
Thanks for this useful suggestion. I have added a control group of reviewers who requested citations to articles other than their own. The words requested were very similar to the previous analysis, hence I’ve needed to reinterpret the results from the text analysis as “please” and “need” are not exclusively used by those requesting selfcitations. I also fixed a minor error in the text analysis concerning the exclusion of abstracts of shorter than 100 characters.
More discussion on the recommendations will help:
For the suggestion that "the reviewers initially see a version of the article with all references blinded and no reference list" the paper says "this involves more administrative work and demands more from peer reviewers". I am afraid this can also degrade the quality of peer review, given that the research cannot be contextualized properly by referees. Referees may not revert back to all their thoughts and evaluations when references are released afterwards.
This is an interesting point, but I don’t think it’s certain that this would happen. For example, revisiting the review may provide a fresh perspective and new ideas; this sometimes happens for me when I review the second version of an article. Ideally an experiment is needed to test this approach, as it is difficult to predict how authors and reviewers will react.
Recommendations for the Authors:
Editorial feedback:
I wonder if the article would benefit from a shorter title, such as the one suggested below. However, please feel free to not change the title if you prefer.
[i] Are peer reviewers influenced by their work being cited (or not)?
I like the slightly simpler: “Are peer reviewers influenced by their work being cited?”
[ii] To better reflect the findings in the article, please revise the abstract along the following lines:
Peer reviewers for journals sometimes write that one or more of their own articles should have been cited in the article under review. In some cases such comments are justified, but in other cases they are not. Here, using a sample of more than 37000 peer reviews for four journals that use open peer review and make all article versions available, we use a matched study design to explore this and other phenomena related to citations in the peer review process. We find that reviewers who were cited in the article under review were less likely to approve the original version of an article compared with reviewers who were not cited (odds ratio = 0.84; adjusted 99.4% CI: 0.69-1.03), but were more likely to approve a revised article in which they were cited (odds ratio = 1.61; adjusted 99.4% CI: 1.16-2.23). Moreover, for all versions of an article, reviewers who asked for their own articles to be cited were much less likely to approve the article compared with reviewers who did not do this (odds ratio = 0.15; adjusted 99.4% CI: 0.08-0.30). However, reviewers who had asked for their own articles to be cited were much more likely to approve a revised article that cited their own articles compared to a revised article that did not (odds ratio = 3.5; 95% CI: 2.0-6.1).
I have re-written the abstract along the lines suggested. I have not included the finding that cited reviewers were less likely to approve the article due to the adjusted 99.4% interval including 1.
[iii] The use of the phrase "self-citation" to describe an author citing an article by one of the reviewers is potentially confusing, and I suggest you avoid this phrase if possible.
I have removed “self-citation” everywhere and instead used “citations to their own articles”.
[iv] I think the captions for figures 2, 3 and 4 from benefit from rewording to more clearly describe what is being shown in the figure. Please consider revising the caption for figure 2 as follows, and revising the captions for figures 3 and 4 along similar lines. Please also consider replotting some of the panels so that the values on the horizontal axes of the top panel align with the values on the bottom panel.
I have aligned the odds and probability axes as suggested which better highlights the important differences. I have updated the figure captions as outlined.
Figure 2: Odds ratios and probabilities for reviewers giving a more or less favourable recommendation depending on whether they were cited in the article.
Top left: Odds ratios for reviewers giving a more favourable (Approved) or less favourable (Reservations or Not approved) recommendation depending on whether they were cited in the article. Reviewers who were cited in version 1 of the article (green) were less likely to make a favourable recommendation (odds ratio = 0.84; adjusted 99.4% CI: 0.691.03), but they were more likely to make a favourable recommendation (odds ratio = 1.61; adjusted 99.4% CI: 1.16-2.23) if they were cited in a subsequent version (blue). Top right: Same data as top left displayed in terms of probabilities. From the top, the lines show the probability of a reviewer approving: a version 1 article in which they are not cited (please give mean value and CI); a version 1 article in which they are cited (mean value and CI); a version 2 (or higher) article in which they are not cited (mean value and CI); and a version 2 (or higher) article in which they are cited (mean value and CI).
Bottom left: Same data as top left except that more favourable is now defined as Approved or Reservations, and less favourable is defined as Not approved. Again, reviewers who were cited in version 1 were less likely to make a favourable recommendation (odds ratio = 0.84; adjusted 99.4% CI: 0.57-1.23),and reviewers who were cited in subsequent versions were more likely to make a favourable recommendation (odds ratio = 1.12; adjusted 99.4% CI: 0.59-2.13).
Bottom right: Same data as bottom left displayed in terms of probabilities. From the top, the lines show the probability of a reviewer approving: a version 1 article in which they are not cited (please give mean value and CI); a version 1 article in which they are cited (mean value and CI); a version 2 (or higher) article in which they are not cited (mean value and CI); and a version 2 (or higher) article in which they are cited (mean value and CI).
This figure is based on an analysis of [Please state how many articles, reviewers, reviews etc are included in this analysis].
In all the panels a dot represents a mean, and a horizontal line represents an adjusted 99.4% confidence interval.
Reviewer #1 (Recommendations for the Authors):
A big recommendation to the author would be to consider putting a lot of the statistical analysis in an appendix and describing the methods and results in more accessible terms in the main text. This would help more readers see the baby through the bath water
I have moved four paragraphs from the methods to the supplement. These are the sample size, the two sensitivity analyses on including co-reviewers and confounding by reviewers’ characteristics, and the analysis examining potential bias for the reviewers with no OpenAlex record.
One possibility, that may have been accounted for, but it is hard to say given the density of the analysis, is the possibility that an author who follows the recommendations to cite the reviewer has also followed all the other reviewer requests. This could account for the much higher likelihood of acceptance. Conversely an author who has rejected the request to cite the reviewer may be more likely to have rejected many of the other suggestions leading to a rejection. I couldn't discern whether the analysis had accounted for this possibility. If it has it need to be said more prominently, if it hasn't this possibility at least needs to be discussed. It would be good to see other alternative explanations for the results discussed (and if possible dismissed) in the discussion section too.
This is an interesting idea. It’s also possible that authors more often accept and include any citation requests as it gives them more license to push back on other more involved changes that they would prefer not to make, e.g., running a new analysis. To examine this would require an analysis of the authors’ responses to the reviewers, and I have now added this as a limitation.
I hope this paper will have an impact on scientific publishing but I fear that it won't. This is no reflection on the paper but a more a reflection on the science publishing system.
I do not have any additional references (written by myself or others!) I would like the author to include
Thanks. I appreciate that extra thought is needed when peer reviewing papers on peer review. I do not know the reviewers’ names! I have added one additional reference suggested by the reviewers which had relevant results on previous surveys of coercive citations for the section on “Related research”.
Reviewer #2 (Recommendations for the Authors):
(1) Would it be possible for the author to control for academic discipline? Some disciplines cite at different rates and have different citation sub-cultures; for example, Wilhite and Fong (2012) show that editorial coercive citation differs among the social science and business disciplines. Is it possible that reviewers from different disciplines just take a totally different view of requesting self-citations?
Wilhite, A.W., & Fong, E.A. 2012. Coercive citation in academic publishing. Science, 335: 542-543.
This is an interesting idea, but the number of disciplines would need to be relatively broad to keep a sufficient sample size. The Catch-22 is then whether broad disciplines are different enough to show cultural differences. Overall, this is an idea for future work.
(2) I would like the author to be much more clear about their results in the discussion section. In line 214, they state that "Reviewers who requested a self-citation were much less likely to approve the article for all versions." Maybe in the discussion some language along the lines of "Although reviewers who requested self-citation were actually much less likely to approve an article, my more detailed analyses show that this was not the case when reviewers requested a self-citation without reason or with the inclusion of coercive language such as 'need' or 'please'." Again, word it as you like, but I think it should be made clear that requests for self-citation alone is not a problem. In fact, I would argue that what the author says in lines 250 to 255 in the discussion reflects that reviewers who request self-citations (maybe for good reasons) are more likely to be the real experts in the area and why those who did not request a self-cite did not notice the omission. It is my understanding that editors are trying to get warm bodies to review and thus reviewers are not all equally qualified. Could it be that requesting self-citations for a good reason is a proxy for someone who actually knows the literature better? I'm not saying this is s fact, but it is a possibility. I get this is said in the abstract, but worth fleshing out in the discussion.
I have updated the discussion after a new text analysis and have addressed this important question of whether self-citations are different from citations to other articles. The idea that some self-citers are more aware of the relevant literature is interesting, although this is very hard to test because they could also just be more aware of their own work. The question of whether self-citations are justified is a key question and one that I’ve tried to address in an updated discussion.
Reviewer #3 (Recommendations for the Authors):
Data and code availablility are in good shape. At a high level, I recommend:
Toning down the interpretation of reviewers' motivation, especially since some of this is mitigated by findings presented in the paper.
I have reworded the discussion and included a warning on the observational study design.
Devote more time detailing exactly what data are being presented in each figure/table and results section as described in more detail in the main review (n, selection criteria, conditional subsetting, etc.).
I agree and have provided more details in each figure legend.
Reviewer #4 (Recommendations for the Authors):
A few aspects of the paper are not clear:
I did not follow Figure 4. Are the "self citation" labels supposed to be "citation to other research"?
Thanks for picking up this error which has now been fixed.
I did not understand how to parse the left column of Figure 2
As per the editor’s suggestion, the figure legend has been updated.
Table 3: Please use different markers for the different curves so that it is clearly demarcated even in grayscale print
I presume you meant Figure 3 not Table 3. I’ve varied the symbols in all three odds ratio plots.
Supplementary S3: Typo "Approvep"
Fixed, thanks.
OTHER CHANGES: As well as the four reviews, my paper was reviewed by an AI-reviewer which provided some useful suggestions. I have mentioned this review in the acknowledgements. I have reversed the order of figure 5 to show the probability of “Approved” as this is simpler to interpret.
