When You Put It that Way: Framing Gender Equality Initiatives to Improve Engagement among STEM Academics
Bibliographic record
Abstract
Abstract A number of high-profile gender equality initiatives (GEIs) are intended to address women's underrepresentation in science. However, attitudes toward such initiatives can be negative. In two experiments with STEM academics, we examined how GEIs can be best framed to improve attitudes toward them. In study 1 (N = 113), we manipulated the framing of GEI leadership (led by a man or woman) and GEI focus (benefitting men and women or benefitting women only). The men were more supportive of GEIs benefitting both men and women because of fewer concerns of unfair treatment and more internal motivations to engage with GEIs. The women's level of support was unaffected by framing. In study 2 (N = 151), we framed GEIs as either supported by university management or not and either internally or externally driven. Support was greater for internally driven GEIs. The impact of management support depended on the academics’ experience with GEIs. This research makes evidence-based recommendations for the implementation of GEIs to improve their effectiveness.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".