Me, myself, and my stereotypes: does retraining gender stereotypes change men’s self-concept?
Bibliographic record
Abstract
Communion – defined as a focus on caring for and connecting with others – is a fundamental value associated with well-being. Yet, men tend to identify with communion significantly less than women do. Although implicit gender stereotypes have been implicated in women’s lower STEM self-concepts, there has been no parallel examination of whether implicit stereotypes constrain men’s lower communal self-concepts. The current research tested whether automatic associations between women and communion (i.e. implicit gender stereotypes) predict and causally shape gender differences in communal self-concepts (i.e. personal identification with communion). Applying balanced identity theory, Study 1 (N = 188) revealed that men are less likely than women to implicitly associate themselves with communion (vs. agency). Critically, this gender difference in communal self-concepts was significantly larger among those with strong implicit communal=female stereotypes. In Study 2 (N = 129), experimentally retraining men to automatically associate communion with men (vs. reinforcing existing implicit communal=female stereotypes) increased men’s own implicit communal self-concepts (i.e. their self-communal associations). These findings address important practical and theoretical questions about how changes to implicit gender stereotypes directly affect men’s implicit self-concepts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".