The influence of gender, social roles, and facial appearance on perceived emotionality
Bibliographic record
Abstract
Abstract One of the most pervasive gender stereotypes in Western culture concerns expectations regarding men's and women's emotionality. Whereas men are expected to be anger prone, women are expected to smile more. At the same time, men are generally perceived as more facially dominant and facially dominant individuals are expected to show more anger. That is, both facial appearance and social role expectations would lead observers to expect men to show more anger. The present research had the goal to disentangle the unique contribution of these two factors. As it is impossible in our society to fully untangle the influence of these factors since they are highly confounded, we created an alien society where these factors could be unconfounded. In this alien world, Deluvia, child rearing is exclusively assumed by a third gender, the caregiver, whereas men and women share the same social roles. The facial appearance of the Deluvians was varied along the dominance continuum. The results showed that facially dominant Deluvians, regardless of gender, were expected to show more anger, disgust, and contempt and less happiness, fear, sadness, and surprise. Also, the nurturing caregivers were expected to show less anger, contempt, and disgust as well as more fear, sadness, and surprise, regardless of facial appearance. No effect of gender per se on perceived emotionality was found. Copyright © 2009 John Wiley & Sons, Ltd.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".