Emotional expressivity in men and women: Stereotypes and self-perceptions
Bibliographic record
Abstract
Three studies were conducted to assess prevalent stereotypes regarding men's and women's emotional expressivity as well as self-perceptions of their emotional behaviour. Emotion profiles were employed to assess both modal emotional reactions and secondary emotional reactions to hypothetical events and personal experiences. In Study 1 we asked how men and women in general would react to a series of hypothetical emotional events. In Study 2 we asked how participants themselves expected to react to these same situations and in Study 3 we asked participants to report a personal emotional event in narrative form. Two gender differences emerged across all three studies. Specifically, women were expected to be more likely to react with sadness to negative emotion-eliciting events in general. They also expected themselves to be more likely to react with sadness as well as to cry and to withdraw more when experiencing negative emotional events. Finally, women report more sadness when describing personal events. In contrast, men were expected to react with more happiness/serenity during negative emotional situations. Also, they expect themselves to react more frequently this way as well as to laugh and smile more and to be more relaxed in negative situations. Finally, men tend to report more happiness when describing negative personal events. In sum, the present study gives a more detailed portrayal of how men and women are expected and expect themselves to react to specific emotional situations and presents some evidence that these expectations may influence the way they reconstruct emotional events from their past.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".