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Record W4292262894 · doi:10.1002/job.2660

Interpersonal emotion regulation strategies: Enabling flexibility in high‐stress work environments

2022· article· en· W4292262894 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Organizational Behavior · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEmotional Labor in Professions
Canadian institutionsÉcole Nationale d'Administration Publique
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFlexibility (engineering)PsychologyInterpersonal communicationProcess (computing)Expression (computer science)Social psychologyWork (physics)CognitionStress (linguistics)Cognitive psychologyEmotion workMatching (statistics)Computer scienceManagement

Abstract

fetched live from OpenAlex

Summary While scholars have demonstrated that emotions play a central role in cognition, behavior, and decision making, most of the studies on emotions in work contexts show that emotions, or their expression, are often suppressed. We thus investigated how workers in high‐stress work environments deal with emotions and remain functional by focusing on the range of extrinsic regulation strategies used by workers in these environments. Drawing from participant observations and in‐depth, semistructured interviews, we show how police officers are flexible in their choices of emotion‐regulation strategies and how contextual factors emerge as the crux of this process. We contribute to the understanding of regulatory flexibility—defined as the process of matching emotion regulation strategies to environmental circumstances as they unfold in real work situations—by identifying two main enabling factors: coregulation and third party interference.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.996

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.026
GPT teacher head0.316
Teacher spread0.290 · 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