The Wisdom to Know the Difference
Bibliographic record
Abstract
The ability to regulate emotions is central to well-being, but healthy emotion regulation may not merely be about using the "right" strategies. According to the strategy-situation-fit hypothesis, emotion-regulation strategies are conducive to well-being only when used in appropriate contexts. This study is the first to test the strategy-situation-fit hypothesis using ecological momentary assessment of cognitive reappraisal-a putatively adaptive strategy. We expected people who used reappraisal more in uncontrollable situations and less in controllable situations to have greater well-being than people with the opposite pattern of reappraisal use. Healthy participants ( n = 74) completed measures of well-being in the lab and used a smartphone app to report their use of reappraisal and perceived controllability of their environment 10 times a day for 1 week. Results supported the strategy-situation-fit hypothesis. Participants with relatively high well-being used reappraisal more in situations they perceived as lower in controllability and less in situations they perceived as higher in controllability. In contrast, we found little evidence for an association between greater well-being and greater mean use of reappraisal across situations.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".