Bystanders to Bias: Witnessing Gendered Microaggressions Affects Men’s and Women’s Outcomes in STEM Small Group Contexts
Bibliographic record
Abstract
= 753), we randomly assigned computer science and engineering students to witness microaggressions targeting female students, or control interactions, using a video manipulation. Witnessing microaggressions-compared to the control-resulted in heightened gender-specific stereotyping concerns, with women being concerned about appearing incompetent and men being concerned with appearing sexist. For both women and men, witnessing microaggressions resulted in decreased enthusiasm for participating in group work. Moreover, for women, the relationship between decreased enthusiasm and witnessing microaggressions was partially mediated by increased concerns about being stereotyped as incompetent. Across the experiments, mixed results emerged regarding the effect of witnessing microaggressions on the recall of engineering content in the video. This research extends previous work focused on personally experiencing microaggressions to merely witnessing them, showing that positivity toward anticipated group work is diminished for both women and men when they see peers engaging in microaggressions.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".