Gender and Geographical Representation on Editorial Board Members of Medical Informatics Journals
Bibliographic record
Abstract
Previous work has suggested that gender and geographical distribution (affiliation) of Editors-in-Chief (EiC) and Editorial Board (EB) members are inequitable in representation of scientific communities, and could benefit from increasing diversity of representation. Specifically, previous studies suggest that male and ethnically white (or non-minoritized groups) are overrepresented. Such differences in representation may potentially influence the scientific and scholarly record. This paper aims to build on pre-existing literature by examining the diversity of representation among EiCs and EB members in the top (Q1) journals in the "Medicine-Health Informatics" category (ranked by SCImago Journal and Country Rank, or SJR) in terms of gender as assessed by genderize.io) and geographical distribution of affiliations. Preliminary findings are consistent with those of previous work on the topic: only 25% (8/32) of the EiCs in the selected journals are female, while females only represent 32.7% (426/1303) of the EB members across journals. Furthermore, the US is highly represented in EBs, with more than half of the members, i.e., 52.2% (698/1337), being US-affiliated. Present results suggest the need for an intentional approach to diversifying representation on editorial boards of medical informatics journals. Such intention can be seen as part of a call to action from important stakeholders, including medical informatics leaders and programs, journal management and publishers, and the medical informatics and scientific community more generally.
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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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".