Emotion Regulation in Achievement Situations: An Integrated Model
Bibliographic record
Abstract
Achievement emotions are critical because of their impact on success and failure in important domains such as learning. These emotions may be modified via emotion regulation (ER). The dominant process model of ER (PMER) proposed by J. Gross, however, provides a domain-general account of ER strategies and has not had substantial contact with theories of achievement emotions such as R. Pekrun’s control-value theory (CVT) and the academic achievement literature. Moreover, ER has not been a focal point of major theories related to achievement emotions, such as CVT. We propose an integrated model of ER in achievement situations (ERAS) that integrates propositions about the generation of emotions from CVT with propositions about how emotions are regulated and types of ER strategies from PMER. The ERAS model also offers new propositions regarding how different achievement situations, object foci, and time frames, as well as discrete emotions with different appraisal patterns, impact ER strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".