On the Nature of Emotion Regulation
Bibliographic record
Abstract
This paper presents a unitary approach to emotion and emotion regulation, building on the excellent points in the lead article by Cole, Martin, and Dennis (this issue), as well as the fine commentaries that follow it. It begins by stressing how, in the real world, the processes underlying emotion and emotion regulation appear to be largely one and the same, rendering the value of the distinction largely for the benefit of analysis. There is an extensive discussion of how the same processes can generate emotions (i.e., are constitutive of emotion) and account for variability of manifestation of emotion in context (i.e., regulate them). Following an extensive review of many of the principles involved in emotion and emotion regulation, the paper presents implications for developmental study of infants and children, includes several methodological recommendations, and concludes with an analysis of the extent to which contemporary affective neuroscience contributes to the study of emotion and emotion regulation.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.016 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".