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Record W2916538243 · doi:10.1177/0963721419827526

Reappraisal Reconsidered: A Closer Look at the Costs of an Acclaimed Emotion-Regulation Strategy

2019· article· en· W2916538243 on OpenAlex
Brett Q. Ford, Allison S. Troy

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Directions in Psychological Science · 2019
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCognitive reframingCognitive reappraisalPsychologyExpressive SuppressionCognitive psychologyEmotional regulationCognitive appraisalStressorCognitionSocial psychologyDevelopmental psychologyClinical psychology

Abstract

fetched live from OpenAlex

Cognitive reappraisal is a common form of emotion regulation that often centers on reframing how one thinks about an emotional situation so that one feels better. Given its demonstrated widespread benefits, two conclusions have been drawn about reappraisal: People can use it easily, and people should use it frequently. We critically examine these conclusions and highlight two fundamental drawbacks of reappraisal: First, people are often unable to use reappraisal successfully, and second, even when successful, using reappraisal to feel better is not always functional. To synthesize current research and inspire future research, we present a conceptual framework that systematically considers these drawbacks and how they may be influenced by individual-centered factors (e.g., the individual’s skill) and situation-centered factors (e.g., a stressor’s intensity) to shape outcomes across time. We then summarize the current literature and highlight the importance of considering reappraisal’s costs and benefits in future research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.056
GPT teacher head0.401
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it