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Record W3217660485 · doi:10.1027/1866-5888/a000295

It's Only Abusive If I Care

2021· article· en· W3217660485 on OpenAlexaff
Kai C. Bormann, Ian R. Gellatly

Bibliographic record

VenueJournal of Personnel Psychology · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAbusive supervisionOrganizational citizenship behaviorPsychologyConservation of resources theorySocial psychologyContext (archaeology)FeelingValue (mathematics)Organizational commitment

Abstract

fetched live from OpenAlex

Abstract. Drawing on conservation of resources (COR) theory, we propose that abusive supervision increases stress responses in targets, which, in turn, diminishes their ability to perform extra- and in-role work behaviors. However, based on COR theory, we argue that followers who are driven by low rather than high organizational concern motives place less value on their work and the social context in which technical activities occur. As such, feeling low organizational concern should make people less susceptible to abusive supervision rather than more so. Thus, organizational concern was proposed to moderate the abuse–stress relationship. Across two multisource studies, we found support for most of our hypotheses. Abusive supervision negatively affected organizational citizenship behaviors via increased stress, and low organizational concern was found to attenuate the detrimental effects of abusive supervision. Implications for leadership literature and future research are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0170.005

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 head0.020
GPT teacher head0.296
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations14
Published2021
Admission routes1
Has abstractyes

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