The regulation of negative and positive affect in daily life.
Bibliographic record
Abstract
Emotion regulation has primarily been studied either experimentally or by using retrospective trait questionnaires. Very few studies have investigated emotion regulation in the context in which it is usually deployed, namely, the complexity of everyday life. We address this in the current paper by reporting findings of two experience-sampling studies (Ns = 46 and 95) investigating the use of six emotion-regulation strategies (reflection, reappraisal, rumination, distraction, expressive suppression, and social sharing) and their associations with changes in positive affect (PA) and negative affect (NA) in daily life. Regarding the relative use of emotion-regulation strategies, a highly similar ordering was found across both studies with distraction being used more than sharing and reappraisal. While the use of all six strategies was positively correlated both within- and between-persons, different strategies were associated with distinct affective consequences: Suppression and rumination were associated with increases in NA and decreases in PA, whereas reflection was associated with increases in PA across both studies. Additionally, reappraisal, distraction, and social sharing were related to increases in PA in Study 2. Discussion focuses on how the current findings fit with theoretical models of emotion regulation and with previous evidence from experimental and retrospective studies.
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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 | 0.005 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".