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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".