Context shapes social judgments of positive emotion suppression and expression.
Bibliographic record
Abstract
It is generally considered socially undesirable to suppress the expression of positive emotion. However, previous research has not considered the role that social context plays in governing appropriate emotion regulation. We investigated a context in which it may be more appropriate to suppress than express positive emotion, hypothesizing that positive emotion expressions would be considered inappropriate when the valence of the expressed emotion (i.e., positive) did not match the valence of the context (i.e., negative). Six experiments (N = 1,621) supported this hypothesis: when there was a positive emotion-context mismatch, participants rated targets who suppressed positive emotion as more appropriate, and evaluated them more positively than targets who expressed positive emotion. This effect occurred even when participants were explicitly made aware that suppressing targets were experiencing mismatched emotion for the context (e.g., feeling positive in a negative context), suggesting that appropriate emotional expression is key to these effects. These studies are among the first to provide empirical evidence that social costs to suppression are not inevitable, but instead are dependent on context. Expressive suppression can be a socially useful emotion regulation strategy in situations that call for it. (PsycINFO Database Record
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".