Acknowledging complexity in evaluation of gender equality interventions
Bibliographic record
Abstract
A collection of evidence on gender equality published in EClinicalMedicine discusses the systemic nature of restrictive gender norms in science and medicine, and calls attention to the importance of institutional interventions to overcome restrictive gender norms[1]. Inequality is deep-rooted in science and medicine while evaluation of interventions show a very slow progress and often unintended consequences [2]. For example, women often undertake a disproportionate amount of gender equality work, institutional gender equality plans can become box-ticking exercises, men can feel discriminated against, and gender can take prevalence over race and class [3].
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 | 0.552 | 0.703 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 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".