Representation of women on editorial boards of ophthalmology journals: protocol for a cross-sectional study
Bibliographic record
Abstract
Introduction There is a notable under-representation of women in leadership positions in ophthalmology despite the increasing number of women as ophthalmologists. Gender inequality in editorial boards of ophthalmology journals has not been investigated on a global scale. This study will aim to evaluate the representation of women as editorial board members in ophthalmology journals across different regions, journal subspecialties and impact factors. Methods and analysis This will be a cross-sectional study describing the gender composition of editorial boards in ophthalmology journals globally. Ulrich’s Periodicals Directory and SCImago Journal & Country Rank will be used to comprehensively identify journals indexed with the keyword, ‘ophthalmology’. All journals with active websites and lists of editorial boards will be included. Journals will be categorised according to the World Bank’s 2021 classification of countries by income and region, and classified into ophthalmology subspecialties based on publication scope. Impact factors will be obtained from Journal Citation Reports. The gender and academic degrees of each editorial board member will be determined based on journal profiles, institutional websites or name query feature on an online interface. The research impact of each editorial board member will be ascertained from the author records on Web of Science. The gender proportion will be presented for all journals combined, and then for journals grouped by regions, subspecialties and impact factors. Editorial board member characteristics including academic degrees and research productivity measures will be compared between men and women. These comparisons will be made using the χ 2 test for categorical variables and the independent samples t-test for continuous variables. Ethics and dissemination This study did not require research ethics approval given the use of publicly available data and lack of human subjects. The results will be presented at scientific meetings and published in peer-reviewed journals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.068 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".