Perceptions and Consequences of Confronting Sexism: A Multi-Method Examination of Context and Confrontier Identities
Bibliographic record
Abstract
Despite the numerous benefits of confronting prejudice, people rarely stand up to expressions of intergroup bias. Across three papers and nine studies, using a multi-method approach spanning scenario studies, reverse correlation paradigms, and an immersive interpersonal interaction, the present research investigated consequences and support for confrontation across confronter identities and contexts and their associated outcomes. In three experiments, Paper 1 examined expectations for confrontation related to a sexist incident, evaluations of the actors across confronting responses, and support for confrontation. These questions were investigated across various confronter identities (female target versus male witness) and context (social versus professional). In four experiments, Paper 2 used a reverse correlation paradigm to explore attributes (i.e., likeability, morality, masculinity, power, and age) associated with mental images of confronters versus nonconfronters of sexism. These perceptions were examined across varying confronter identities (female target, male witness, self). In two experiments, Paper 3 implemented an online chat interaction to investigate how confronting or passive responses affected perceptions of competence and likeability, support for confrontation, and leadership outcomes. Together, the results provide novel evidence for not only the disadvantages but also the advantages associated with confronting sexism across confronter identity and contexts. Benefits of confronting, particularly in domains related to power, competence, and leadership are highlighted.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".