Two brief interventions to mitigate a “chilly climate” transform women’s experience, relationships, and achievement in engineering.
Bibliographic record
Abstract
In a randomized-controlled trial, we tested 2 brief interventions designed to mitigate the effects of a “chilly climate” women may experience in engineering, especially in male-dominated fields. Participants were students entering a selective university engineering program. The social-belonging intervention aimed to protect students’ sense of belonging in engineering by providing a nonthreatening narrative with which to interpret instances of adversity. The affirmation-training intervention aimed to help students manage stress that can arise from social marginalization by incorporating diverse aspects of their self-identity in their daily academic lives. As expected, gender differences and intervention effects were concentrated in male-dominated majors (20% women). In these majors, compared with control conditions, both interventions raised women’s school-reported engineering grade-point-average (GPA) over the full academic year, eliminating gender differences. Both also led women to view daily adversities as more manageable and improved women’s academic attitudes. However, the 2 interventions had divergent effects on women’s social experiences. The social-belonging intervention helped women integrate into engineering, for instance, increasing friendships with male engineers. Affirmation-training helped women develop external resources, deepening their identification with their gender group. The results highlight how social marginalization contributes to gender inequality in quantitative fields and 2 potential remedies.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".