Global evidence of gender inequity in academic health research: a living scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to describe the global evidence of gender inequity among individuals with appointments at academic institutions that conduct health research, and examine how gender intersects with other social identities to influence outcomes. INTRODUCTION: The gender demographics of universities have shifted, yet the characteristics of those who lead academic health research institutions have not reflected this change. Synthesized evidence will guide decision-making and policy development to support the progress of gender and other under-represented social identities in academia. INCLUSION CRITERIA: This review will consider any quantitative, qualitative, or mixed methods primary research that reports outcome data related to gender equity and other social identities among individuals affiliated with academic or research institutions that conduct health research, originating from any country. METHODS: The JBI Manual for Evidence Synthesis and the Cochrane Collaboration's guidance on living reviews will inform the review methods. Information sources will include electronic databases, unpublished literature sources, reference scanning of relevant systematic reviews, and sources provided by experts on the research team. Searches will be run regularly to monitor the development of new literature and determine when the review will be updated. Study selection and data extraction will be conducted by two reviewers working independently, and all discrepancies will be resolved by discussion or a third reviewer. Data synthesis will summarize information using descriptive frequencies and simple thematic analysis. Results will be reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension to scoping reviews. REGISTRATION: Open Science Framework: https://osf.io/8wk7e/.
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 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 | 0.173 | 0.169 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.087 | 0.022 |
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".