Social Support Predicts Differential Use, But Not Differential Effectiveness, of Expressive Suppression and Social Sharing in Daily Life
Bibliographic record
Abstract
While emotion regulation often happens in the presence of others, little is known about how social context shapes regulatory efforts and outcomes. One key element of the social context is social support. In two experience sampling studies (Ns = 179 and 123), we examined how the use and affective consequences of two fundamentally social emotion regulation strategies—social sharing and expressive suppression—vary as a function of perceived social support. Across both studies, we found evidence that perceived social support predicted variation in people’s use of these strategies, such that higher levels of social support predicted more sharing and less suppression. However, we found only limited and inconsistent support for context-dependent affective outcomes of suppression and sharing: suppression was associated with better affective consequences in the context of higher perceived social support in Study 1, but this effect did not replicate in Study 2. Taken together, these findings suggest that the use of social emotion regulation strategies appears to depend on contextual variability in social support, whereas their effectiveness does not. Future research is needed to better understand the circumstances in which context-dependent use of emotion regulation may have emotional benefits, accounting for personal, situational, and cultural factors.
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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 | 0.007 |
| 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 | 0.001 |
| 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".