Focusing on Inclusion (vs. Leadership) Boosts Gender Bias Recognition and Intended Action
Bibliographic record
Abstract
ABSTRACT When gender bias arises at work, inaction all too often follows. Can simple prompts to consider inclusion or leadership orient observers toward combatting bias? Four experiments using six samples (three preregistered; N = 4712) tested effects of a focus manipulation among people observing workplace sexism. Before viewing a video of a sexist episode, participants were randomly assigned via a one‐sentence instruction (Studies 1 and 2) or questions about their workplace (Studies 3 and 4) to focus on inclusion, leadership, or no specific focus (control). In mega‐analyses across studies, focusing on inclusion (vs. leadership) led participants to perceive more gender bias in the situation, blame the target of sexism less, and spontaneously express target support intentions. Focusing on inclusion (vs. leadership) improved impressions (Studies 1–3), affiliation (Studies 2 and 3), and pay allocation (Study 4) for the target relative to the perpetrator. We discuss implications for fostering more inclusive workplace environments.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".