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How Do You Feel Today? Emotion Regulation and Work Outcomes

2024· article· en· W4400440177 on OpenAlexaff
Xiaoxia Zhu, Joohan Lee, Huong Le, Neal M. Ashkanasy, Alannah E. Rafferty, Ashlea C. Troth, Peter J. Jordan, Sandra Merino Verona, Ramón Rico, Sjir Uitdewilligen, Manuel Quintana‐Díaz, Mahbubul Alam, Elena Maria Wong, Michael Donald Caligiuri, Marla L. White

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEmotional Labor in Professions
Canadian institutionsAlgoma UniversityEssar Steel Algoma (Canada)
Fundersnot available
KeywordsWork (physics)PsychologyCognitive psychologySocial psychologyApplied psychologyEngineering

Abstract

fetched live from OpenAlex

A variety of occupational aspects and factors have been much changed since the unexpected global crisis (e.g., Covid-19 pandemic) and the advancement of artificial intelligence. Despite research efforts and interests in those new areas, these happenings paradoxically revealed that emotion regulation which features human beings is still important across diverse workplaces and technological breakthrough cannot substitute for the roles of employees’ emotion regulation in workplaces. Given the importance of emotion regulation at work and the Annual Meeting theme of

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score0.415

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.322
Teacher spread0.293 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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