Loosening the GRIP (Gender Roles Inhibiting Prosociality) to Promote Gender Equality
Bibliographic record
Abstract
Prosociality is an ideal context to begin shifting traditional gender role stereotypes and promoting equality. Men and women both help others frequently, but assistance often follows traditional gender role expectations, which further reinforces restrictive gender stereotypes in other domains. We propose an integrative process model of gender roles inhibiting prosociality (GRIP) to explain why and how this occurs. We argue that prosociality provides a unique entry point for change because it is (a) immediately rewarding (which cultivates positive attitude formation), (b) less likely to threaten the gender status hierarchy, and therefore less susceptible to social backlash (which translates into less restrictive social norms), and (c) a skill that can be learned (which leads to stronger beliefs in one's own ability to help). Using the GRIP model, we derive a series of hypothesized interventions to interrupt the self-reinforcing cycle of gender role stereotyping and facilitate progress toward broader gender equality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".