Toward a Personalized Science of Emotion Regulation
Bibliographic record
Abstract
The ability to successfully regulate emotion plays a key role in healthy development and the maintenance of psychological well-being. Although great strides have been made in understanding the nature of regulatory processes and the consequences of deploying them, a comprehensive understanding of emotion regulation that can specify what strategies are most beneficial for a given person in a given situation is still a far-off goal. In this review, we argue that moving toward this goal represents a central challenge for the future of the field. As an initial step, we propose a concrete framework that (i) explicitly considers emotion regulation as an interaction of person, situation, and strategy, (ii) assumes that regulatory effects vary according to these factors, and (iii) sets as a primary scientific goal the identification of person-, situation-, and strategy-based contingencies for successful emotion regulation. Guided by this framework, we review current questions facing the field, discuss examples of contextual variation in emotion regulation success, and offer practical suggestions for continued progress in this area.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".