The misjudgment of men: Does pluralistic ignorance inhibit allyship?
Bibliographic record
Abstract
(Miller & McFarland, 1991; Prentice, 2007; Prentice & Miller, 1993) inhibits allyship. We first hypothesized that, if men rarely enact allyship toward women (e.g., in science, technology, engineering, and mathematics [STEM] fields), people will underestimate men's beliefs that sexism is problematic. Second, these misperceptions might then predict men's (and women's) own inaction, despite their private beliefs about gender bias. Additionally, men with higher masculinity concerns might be particularly inhibited from enacting allyship by their belief that other men are unconcerned with gender bias. All three studies yielded evidence that men and women underestimate men's privately expressed concerns about gender bias in STEM contexts. In correlational analyses, Studies 1 and 2 also revealed that among men high in precarious masculinity concerns, the belief that other men do not see bias as a problem predicted lower allyship intentions, controlling for their own beliefs about gender bias. Although experimentally correcting these beliefs with data changed perceptions (Studies 2 and 3), this was insufficient to increase allyship. Rather, in an ecologically valid behavioral paradigm (Study 3), allyship behavior was elevated when participants observed others confronting versus not confronting sexism. These findings suggest that perceptions of men's average beliefs inhibit allyship intentions; however, merely correcting these misperceptions might not be enough to motivate actual confrontation. We discuss the implications of these findings for a pluralistic ignorance account of allyship inhibition and for practical interventions aimed at promoting allyship. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.004 | 0.018 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".