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. 2020 Jan 28;11(8):2277-2301.
doi: 10.1039/c9sc04090k. eCollection 2020 Feb 28.

Is there a gender gap in chemical sciences scholarly communication?

A E Day  1 , P Corbett  1 , J Boyle  1
Affiliations

Is there a gender gap in chemical sciences scholarly communication?

A E Day et al. Chem Sci. .

Abstract

The Royal Society of Chemistry is committed to investigating and addressing the barriers and biases which face women in the chemical sciences. The cornerstone of this is a thorough analysis of data regarding submissions, review and citations for Royal Society of Chemistry journals from January 2014 until July 2018, since the number and impact of publications and citations are an important factor when seeking research funding and for the progression of academic career. We have applied standard statistical techniques to multiple data sources to perform this analysis, and have investigated whether interactions between variables are significant in affecting various outcomes (author gender; reviewer gender; reviewer recommendations and submission outcome) in addition to considering variables individually. By considering several different data sources, we found that a baseline of approximately a third of chemistry researchers are female overall, although this differs considerably with Chemistry sub-discipline. Rather than one dominant bias effect, we observe complex interactions and a gradual trickle-down decrease in this female percentage through the publishing process and each of these female percentages is less than the last: authors of submissions; authors of RSC submissions which are not rejected without peer review; authors of accepted RSC publications; authors of cited articles. The success rate for female authors to progress through each of these publishing stages is lower than that for male authors. There is a decreasing female percentage when progressing through from first authors to corresponding authors to reviewers, reflecting the decreasing female percentage with seniority in Chemistry research observed in the "Diversity landscape of the chemical sciences" report. Highlights and actions from this analysis form the basis of an accompanying report to be released from the Royal Society of Chemistry.

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Figures

Fig. 1
Fig. 1. Percentage breakdown of chemistry researcher gender by HESA contract level.
Fig. 2
Fig. 2. Percentage breakdown of chemistry researcher gender by RSC membership level.
Fig. 3
Fig. 3. Percentage breakdown of corresponding author gender with number of submissions.
Fig. 4
Fig. 4. Total breakdown of submissions by corresponding author gender and country for top 20 countries (including unknown gender).
Fig. 5
Fig. 5. Total (left-hand) and percentage (right-hand) breakdown of corresponding author gender calculated by ONS method (top row) and gender-guesser method (bottom row) with continent of corresponding author address.
Fig. 6
Fig. 6. Percentage breakdown of submissions by corresponding author gender and number of authors (having omitted unknown gender).
Fig. 7
Fig. 7. Percentage of female authors with author position and number of authors – asterisks indicate the significance of the binomial p-value: *** is highly significant (p < 0.001); ** is very significant (p < 0.01); * is significant (p < 0.05); and no value implies not significant.
Fig. 8
Fig. 8. Percentage breakdown of submissions by corresponding author gender and date.
Fig. 9
Fig. 9. Percentage breakdown of submissions by corresponding and first author gender and chemistry sub-discipline.
Fig. 10
Fig. 10. Binomial GLM model of corresponding author gender of original submissions, chemistry sub-discipline and journal impact factor (Model: CorrAuthorGender ∼ Category * ImpactFactor). ANOVA Pr(>Chi): category is highly significant (0.00 × 100); ImpactFactor is highly significant (0.00 × 100); category: ImpactFactor interactions are highly significant (3.75 × 10–97).
Fig. 11
Fig. 11. Percentage breakdown of submissions by editor gender and journal editorial model.
Fig. 12
Fig. 12. Binomial GLM model of proportion of submissions rejected without peer review and corresponding author gender controlled by whether the publication is single-authored or not (Model: RejectedWithoutPeerReview ∼ SingleAuthor * CorrAuthorGender). ANOVA Pr(>Chi): SingleAuthor is highly significant (0.000); CorrAuthorGender is highly significant (0.000); SingleAuthor: CorrespondingAuthorGender is not significant (0.903).
Fig. 13
Fig. 13. Binomial GLM models of proportion of original submissions rejected without peer review. (a) – Binomial GLM model of proportion of original submissions rejected without peer review, corresponding author gender and editor gender (Model: RejectedWithoutPeerReview ∼ CorrAuthorGender * EditorGender). ANOVA Pr(>Chi): CorrAuthorGender is highly significant (1.71 × 10–20); EditorGender is highly significant (8.98 × 10–79); CorrespondingAuthorGender: EditorGender is not significant (0.76). (b) – Binomial GLM model of proportion of original submissions rejected without peer review, corresponding author gender and journal editorial model (Model: RejectedWithoutPeerReview ∼ CorrAuthorGender * EditorialModelOfJournal). ANOVA Pr(>Chi): CorrAuthorGender is highly significant (6.18 × 10–41); EditorialModelOfJournal is highly significant (0.00); CorrAuthorGender: EditorialModelOfJournal is highly significant (3.07 × 10–4).
Fig. 14
Fig. 14. Binomial GLM model of proportion of reviews. (a) – Binomial GLM model of proportion of review invitations by female reviewers, corresponding author gender and reviewer response (Model: ReviewerGender ∼ CorrAuthorGender * Response). ANOVA Pr(>Chi): CorrAuthorGender is highly significant (1.77 × 10–161); Response is highly significant (2.13 × 10–28); CorrAuthorGender: Response is highly significant (3.08 × 10–8). (b) – Binomial GLM model of proportion of reviews by female reviewers, corresponding author gender and editor gender (Model: ReviewerGender ∼ CorrAuthorGender * EditorGender). ANOVA Pr(>Chi): CorrAuthorGender is highly significant (1.64 × 10–64); EditorGender is highly significant (1.86 × 10–46); CorrAuthorGender: EditorGender is significant (9.04 × 10–3).
Fig. 15
Fig. 15. Gender breakdowns of reviews. (a) – Percentage breakdown of reviews by reviewer gender and date. (b) – Total numbers of reviews by reviewer gender and number of reviewers for that submission. (c) – Percentage breakdown of reviews by reviewer gender and number of revisions.
Fig. 16
Fig. 16. Percentage breakdown of reviews by reviewer gender for each chemistry sub-discipline in comparison with percentage of submissions from female corresponding authors.
Fig. 17
Fig. 17. Multinomial GLM model of reviewer recommendations and corresponding author gender (Model: ReviewerRecommendation ∼ CorrAuthorGender).
Fig. 18
Fig. 18. Multinomial GLM model of reviewer recommendations and reviewer gender (Model: ReviewerRecommendation ∼ ReviewerGender).
Fig. 19
Fig. 19. Multinomial GLM model of first round reviewer recommendations, corresponding author gender and reviewer gender (female reviewers on top row and male reviewers beneath) (Model: ReviewerRecommendation ∼ ReviewerGender * CorrAuthorGender). ANOVA Pr(>Chi): ReviewerGender: CorrespondingAuthorGender interactions are highly significant (0.00 × 100).
Fig. 20
Fig. 20. Binomial GLM models of “Status and recommendation agree” variable and reviewer gender. (a) – Binomial GLM model of “Status and recommendation agree” variable, reviewer gender and corresponding author gender (Model: StatusAndRecommendationAgree ∼ ReviewerGender * CorrAuthorGender). ANOVA Pr(>Chi): ReviewerGender is significant (0.0018); CorrAuthorGender is not significant (0.643); ReviewerGender: CorrAuthorGender is not significant (0.843). (b) – Binomial GLM model of “Status And Recommendation Agree” variable, reviewer gender and editor gender (Model: StatusAndRecommendationAgree ∼ ReviewerGender * EditorGender). ANOVA Pr(>Chi): ReviewerGender is highly significant (1.09 × 10–5); EditorGender is highly significant (1.05 × 10–9); ReviewerGender: EditorGender is highly significant (1.42 × 10–7).
Fig. 21
Fig. 21. Percentage breakdown of accepted submissions by corresponding author gender and chemistry sub-discipline in comparison to that of all original submissions.
Fig. 22
Fig. 22. Binomial GLM model of corresponding author gender of accepted submissions, chemistry sub-discipline and journal impact factor (Model: CorrAuthorGender ∼ Category * ImpactFactor). ANOVA Pr(>Chi): Category is highly significant (0.00 × 100); ImpactFactor is highly significant (0.00 × 100); Category: ImpactFactor interactions are highly significant (3.18 × 10–73).
Fig. 23
Fig. 23. Percentage breakdown of submissions by corresponding author gender and final number of revisions.
Fig. 24
Fig. 24. Gender percentage breakdown of corresponding and first authors of cited and citing articles of citations in comparison to those of all publications.
Fig. 25
Fig. 25. Percentage breakdown of articles' corresponding author gender by number of RSC citations to it.
Fig. 26
Fig. 26. Binomial GLM model of citation success of published articles and corresponding author gender controlled by whether the article was unanimously accepted in its first revision (Model: Cited ∼ UnanimousAccept * CorrAuthGender). ANOVA Pr(>Chi): UnanimousAccept is significant (0.00); CorrAuthorGender is significant (3.22 × 10–27); UnanimousAccept: CorrAuthorGender interactions is not significant (0.408).
Fig. 27
Fig. 27. Percentage breakdown of cited corresponding author gender of citations by the cited article's publication year.
Fig. 28
Fig. 28. Breakdown of gender of de-duplicated authors of all citing articles and number of self-citations in RSC citation data set.
Fig. 29
Fig. 29. Gender percentage breakdown of cited corresponding authors of citations by chemistry sub-discipline in comparison to that of corresponding authors of all accepted submissions.
Fig. 30
Fig. 30. Binomial GLM model of corresponding author gender of citations, chemistry sub-discipline and journal impact factor (Model: CitedCorrAuthorGender ∼ Category * ImpactFactor). ANOVA Pr(>Chi): category is highly significant (3.26 × 10–66); ImpactFactor is highly significant (1.96 × 10–17); category: ImpactFactor interactions are highly significant (2.49 × 10–14).
Fig. 31
Fig. 31. Total breakdown of living Chemists with highest H-index ranking by gender and H-index.
Fig. 32
Fig. 32. Female percentage of corresponding authors throughout publication process.
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References

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