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Record W4402656854 · doi:10.1002/job.2837

Getting away “Scott” (but not Susan) free: The effects of safety‐specific abusive supervision and supervisor gender on follower attributions and safety outcomes

2024· article· en· W4402656854 on OpenAlexaff
John Fiset, Alyson Byrne

Bibliographic record

VenueJournal of Organizational Behavior · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsMemorial University of NewfoundlandSaint Mary's University
Fundersnot available
KeywordsSupervisorAttributionAbusive supervisionPsychologySocial psychologyApplied psychologyManagementEconomics

Abstract

fetched live from OpenAlex

Summary While most research emphasizes the harmful effects of abusive supervision, we argue that certain contextual factors—specifically hazardous work environments and supervisor gender—may lead abusive supervision to be perceived as driven by performance promotion intentions as opposed to injury initiation intentions. We introduce the concept of Safety‐Specific Abusive Supervision (SSAS), which we define as the extent to which a supervisor's active response to safety incidents is perceived by employees as abusive. Drawing from event system theory and research on attributions of abusive supervision, we theorize that when supervisors engage in SSAS, employees are more likely to attribute their behavior to performance promotion rather than injury initiation, perceiving the supervisors' actions as a means to keep them safe rather than to cause harm. We predict that performance promotion attributions mediate the relationship between SSAS and safety performance outcomes, namely safety voice and perceived supervisor safety commitment. However, consistent with role congruity theory, we hypothesize that this relationship is only true for male supervisors and not for female supervisors. Across three studies (two experimental studies and one field study), we largely find support for our hypotheses.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.017
GPT teacher head0.286
Teacher spread0.269 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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