Racial, gender, sexual, and disability identities of the Journal of the Medical Library Association’s editorial board, reviewers, and authors
Bibliographic record
Abstract
The Journal of the Medical Library Association (JMLA) recently issued a call for submissions that recognize and address social injustices; speak to diversity, equity, and inclusion in our workforce and among our user populations; and share critical perspectives on health sciences librarianship as well as those on any topic within JMLA’s scope written by authors who are Black, Indigenous, or People of Color. We also committed to creating more equitable opportunities for authors, reviewers, and editorial board members from marginalized groups. As part of this effort, we conducted a demographic survey of all individuals who served as a member of the JMLA editorial board or reviewer or had submitted a manuscript to JMLA between 2018 and 2020. We found that most survey respondents are white, heterosexual, women and do not identify with a disability, meaning that JMLA is missing out on a diversity of perspectives and life experiences that could improve the journal’s processes and policies, enrich its content, and accelerate the research and practice of health sciences librarianship. Therefore, to avoid perpetuating or aggravating systemic biases and power structures in scholarly publishing or health sciences librarianship, we pledge to take concrete steps toward making JMLA a more diverse and inclusive journal.
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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 | 0.018 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".