Uncovering Pluralistic Ignorance to Change Men’s Communal Self-descriptions, Attitudes, and Behavioral Intentions
Bibliographic record
Abstract
Gender norms can lead men to shy away from traditionally female roles and occupations in communal HEED domains (Healthcare, Early Education, Domestic sphere) that do not fit within the social construct of masculinity. But to what extent do men underestimate the degree to which other men are accepting of men in these domains? Building on research related to social norms and pluralistic ignorance, the current work investigated whether men exhibit increased communal orientations when presented with the true norms regarding men’s communal traits and behaviors versus their perceived faulty norms. Study 1 (N = 64) revealed that young Belgian men indeed perceive their peers to hold more traditional norms regarding communal and agentic traits than their peers actually hold. Study 2 (N = 319) presented young Belgian men with the data collected in Study 1 to manipulate exposure to men’s actual normative beliefs (i.e., what men truly think), their perceived norms (i.e., what men believe other men think), or a no information control. When men were presented with actual rather than perceived norms, they altered their own self-descriptions, future behavioral intentions, and broader gender-related social attitudes in a more communal direction. In particular, men who were presented with information about men’s actual beliefs regarding the compatibility between communal and agentic traits exhibited the strongest movement towards a more communal orientation. The findings show that participants in conditions that uncover pluralistic ignorance adapted their attitudes and behaviors to be more in line with the actual norm: adopting a more communal self-concept, having lower intentions to hide future communal engagement, and supporting more progressive gender-related social change. The results are discussed in terms of influences of norms on men’s communal orientations and broader attitudes towards gender-related social change, and the down-stream implications for increased gender-equality in HEED domains where men remain highly underrepresented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.002 | 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".