Resistance towards increasing gender diversity in masculine domains: The role of intergroup threat
Bibliographic record
Abstract
Efforts to increase diversity can often be met with resistance amongst high-status groups. Despite this, little is known about majority-group responses towards increasing gender diversity, and the psychological mechanisms underlying them. Across five studies, we extended intergroup threat theory to advance understanding of resistance towards gender diversity amongst men in masculine domains (Studies 1–3 and 5) and amongst women in feminine domains (Study 4). Experimental evidence from male STEM students (Study 1) and professionals (Studies 2 and 5) revealed that realistic threats underlie resistance. Experimentally reducing realistic threat (<i>N</i> = 165) reduced negative reactions. Whereas realistic-threat-based resistance towards increasing gender diversity did not extend to women in female-dominated domains (Study 4, <i>N</i> = 105), there was a tendency for women high in ingroup identity to show a similar pattern to men. We discuss how we advance theory on diversity resistance, and discuss strategies which may effectively reduce resistance.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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; a candidate call from one teacher head, 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".