A threat in the network: STEM women in less powerful network positions avoid integrating stereotypically feminine peers
Bibliographic record
Abstract
Integrating social identity threat and structural hole theories, this work examines how social network positions affect group-based identity threats. For individuals less well positioned to bridge (or “broker”) relations between unconnected friends, stigma-by-association concerns may constrain affiliation with stereotypic targets. Three experiments ( Ns = 280, 232, 553) test whether women (vs. men) in male-dominated STEM (vs. female-dominated) majors avoid befriending a female target with feminine-stereotypic (vs. STEM-stereotypic) interests. Only STEM women with less brokerage (i.e., less ability to manage introductions to unconnected friends) in their existing friendship networks avoided befriending (pilot experiment) and socially integrating (Experiments 1 and 2) feminine- (vs. STEM-) stereotypic targets, despite standardized target similarity and competence. STEM women in particular anticipated steeper reputational penalties for befriending stereotypically feminine peers (Experiment 2). Social identity threat may lead women in STEM—especially those lacking brokerage—to exclude stereotypically feminine women from social networks, reinforcing stereotypes of women and STEM fields.
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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 | 0.002 |
| 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".