Towards inclusive authorship: Analyzing author representation in PLOS Global Public Health front matter content
Bibliographic record
Abstract
Underrepresentation and lack of inclusion of Global South researchers have been key shortcomings in global health publications. This has contributed to epistemic injustice in global health and impacted evidence informed policymaking. PLOS Global Public Health (GPH)was launched in 2021 with the goal of charting a new path towards equity, diversity and inclusion in global health publications. The journal also invited independent assessments of its progress. This study analyses commissioned 136 front matter content (opinions, reviews, and essays) and a total of 878 authors published in PLOS GPH between October 2021 and December 2024. Using publicly available data from the journal website and online profiles, we examined authorship representation based on World Bank country income classification, gender, and Indigeneity. Additionally, we examined article content in terms of country focus and topics covered. We inferred gender by reviewing public profiles for gendered prefixes and pronouns and when unavailable by using genderize.io. We analyzed for Indigeneity by reviewing authors' public profiles. Our results indicate that 609 of 878 (69%) of authors for the commissioned content were affiliated with high income countries. Under gender representation, 403 of 878 (46%) authors identified as women compared to 471 of 878 (54%) as men. Only 7 of 135 (5%) first authors and 6 of 117 (5%) senior authors publicly identified as Indigenous. While most articles had a global focus (78 of 136, or 57%), 46 of 136 (34%) focused on the Global South, and 12 of 136 (8%) on the Global North. Global South affiliated authors were better represented in articles pertaining to the Global South, comprising on average 43% of authorship compared to an overall average of 30%. To advance equity, journals should commission more content from Global South authors and actively invite contributions from Indigenous and gender-diverse authors on topics relevant to their communities.
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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 | 0.046 | 0.328 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".