PONE-D-21-32854Avoiding bias when inferring race using name-based approachesPLOS ONE

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“VL acknowledges funding from the Canada Research Chairs program, https://www.chairs-chaires.gc.ca/,  (grant # 950-231768), DK acknowledges funding from the Luxembourg National Research Fund, https://www.fnr.lu/, under the PRIDE program (PRIDE17/12252781).”

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The manuscript "Avoiding bias when inferring race using name-based approaches" discusses and analyzes the problems of bibliometric analyses that include racial classifications on a conceptual and methodological level. The authors conclude with dos and don'ts for such analyses.

Overall, the manuscript is well written and should be of interest to the readers of PLoS One.

I wonder about the section regarding imputation of missing data. Maybe, the authors could provide advice on whether imputation should be done at all. It seems to be taken for granted that some imputation method should be used. Is imputation from a proper distribution better than no imputation at all? Authors without proper assignment could be just removed from the data set. What would be the advantages and disadvantages?

Reviewer #2: The paper investigates different approaches for inferring the perceived race of authors in the U.S. This can serve as an important methodological basis for large-scale empirical analyses on racial inequalities in the science system. The paper presents a compelling approach to this research question and valuable conclusions for inferring perceived race in bibliometric data. However, I recommend to address the following points before publication.

152-158: At this point, it was difficult for me to get an idea of what the simulated data is used for. A sentence after "First, to test the interaction between given and family names distributions, we simulate a dataset that covers most of the possible combinations" could help to clarify this (e.g. something like "This step is only used to determine how to combine given and family names for inferring race").

169-170: "Questions now include both racial and ethnic origin, placing "Hispanic" outside racial categories. The racial categories in both datasets include Hispanic as a category, ...". At first sight, this sounds contradictory ("Hispanic" is not in racial categories, and at the same time "Hispanic" is used as a category). I would suggest to clarify here that "Hispanic" is not a racial category in the original US Census data, but you use it as a racial category in your datasets. There are also no quotes around "Hispanic" in line 170, while this is usually the case in line 169.

186-187: This implies that only first authors are considered for your analyses. This restriction should be mentioned explicitly, and also why you chose to do so (and did not include other author positions).

193-222: This part seems to better fit in the "Methods" section than the "Data" section. Unless there are good reasons to keep it in the "Data" section, you may want move this part.

226: In the "Methods" section, it is unclear which particular approaches you finally use in your empirical analyses. In particular, which of the three weighting schemes did you finally use for your analyses? I think a concise list of the approaches you use would be helpful for the reader.

252: Use "n" instead of "c" in the summation notation for consistency with the formula in line 245 (or vice versa).

254: "for both given (family) names" looks like it is a measure for two given names or two family names. But as far as I understand it, this weight combines the given and the family name of one person. Should this be "for both given and family names"?

259: Which value did you finally use for exp?

264-272: Which color pattern should be observed in order to have a good approach? Have you tried exponent values > 2? If not, why?

290: ";" -> "."

304-307: A more detailed explanation of how the given name distribution has been normalized and how this affects the results would be helpful here.

Figure 3: How are the values on the y-axis (ratio) calculated, and how does this measure over-/underrepresentation? The frequencies of given/family names that are shown in the upper plots provide important information, but the distribution is difficult to inspect for thresholds > 90%. Can this be visualized in a form that better shows this part of the distribution (e.g. in an extra figure)?

399-416: It is a good point and convincingly shown by the results presented here that simply imputing based on the Census data should be avoided. But imputing based on the distributions in the bibliometric data for known names may also introduce biases. This would be the case if the probability for missing names correlates with the race category (a reason for this might be the development over time of both the probability to have full names in the database and the distribution across race categories). I would argue one has to be very cautious when imputing bibliometric data, because usually these data only provide a limited amount of metadata that can be used for imputation. Given the usually large number of cases in bibliometric data, it is probably not necessary to impute data in most cases. I would argue that it is more important and transparent to discuss possible biases for a particular research question introduced due to missing data than trying to impute the data. I think your results also provide a very good basis for such a discussion with regard to inferring race.

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Avoiding bias when inferring race using name-based approaches

PONE-D-21-32854R1

Dear Dr. Kozlowski,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Lutz Bornmann

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: (No Response)

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If you choose “no”, your identity will remain anonymous but your review may still be made public.

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Reviewer #1: No

Reviewer #2: No