Adding Fuel to the Collective Fire: Stereotype Threat, Solidarity, and Support for Change
Bibliographic record
Abstract
We hypothesize a yet-unstudied effect of experiencing systemic stereotype threat on women’s collective action efforts: igniting women’s support for other women and motivation to improve organizational gender balance. Hypotheses are supported in two surveys (Study 1: N = 1,365 business school alumnae; Study 2: N = 386 women Master of Business Administration [MBA]), and four experiments (Studies 3–6; total N = 1,897 working women). Studies 1 and 2 demonstrate that experiencing stereotype threat is negatively associated with women’s domain-relevant engagement (supporting extant work on the negative effects of stereotype threat), but positively associated with women’s support and advocacy of gender balance. Studies 3 to 6 provide causal evidence that stereotype threat activation leads to greater attitudes and intentions to support gender balance, ruling out negative affect as an alternative explanation and identifying ingroup solidarity as a mechanism. We discuss implications for working women, women leaders, and organizations striving to empower their entire workforce through developing equitable and inclusive practices.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".