The regulation of negative and positive affect in daily life.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it