Reappraisal affordances: a replication of Suri et al. (2018) and investigation of alternate predictors of reappraisal choice
Bibliographic record
Abstract
Reappraisal affordances have recently emerged as an important predictor of emotion regulation choice . In a pre-registered replication of study 4 of Suri et al., 2018, we assessed the role of affordances and several other predictors of regulation choice. Participants (N = 315) read one of eight vignettes that varied in reappraisal affordance (high or low) and intensity (high or low). For each vignette, they rated hedonic and instrumental motives, affordances, intensity, importance, and long-term implications. One week later, participants re-read the vignette, chose between reappraisal and distraction, and rated their likelihood of using each strategy. Unexpectedly, participants rated predicted high affordance vignettes as lower in affordance than predicted low affordance vignettes. This difference from the original study may be due to sample differences: in the original study, participants were employees in a specific workplace and several vignettes targeted workplace activities. Nonetheless, we replicated the original finding that reappraisal affordances predicted reappraisal choice. The result held even when controlling for other contextual variables, which played a limited role in predicting emotion regulation. The results highlight the need to consider multiple aspects of context, including the research setting, when examining predictors of emotion regulation choice.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".