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Record W4224316050 · doi:10.1111/beer.12436

Exposure to workplace bullying and negative gossip behaviors: Buffering roles of personal and contextual resources

2022· article· en· W4224316050 on OpenAlexafffundabout
Dirk De Clercq

Bibliographic record

VenueBusiness Ethics the Environment & Responsibility · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsBrock University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGossipWorkplace bullyingPsychologySocial psychologyFaithWork (physics)Religiosity

Abstract

fetched live from OpenAlex

Abstract This study adds to business ethics research by investigating how employees' exposure to workplace bullying might spur their negative gossip behaviors, as well as how this effect might be buffered by their access to two personal resources (religiosity and innovation propensity) and two contextual resources (work meaningfulness and trust in top management). Survey data collected among Canadian‐based employees who work in the religious sector reveal that workplace bullying increases the likelihood that they spread negative rumors about other organizational members, but this effect is weaker when employees (1) can draw from their religious faith, (2) are motivated to generate innovative ideas, (3) derive meaning from their work, and (4) have confidence in the trustworthiness of top management. For management scholars and practitioners, this study thus pinpoints different resources that diminish the risk that workplace bullying infuses work environments with even more negative energy, as might occur if bullying spills over into additional, negative gossip behaviors.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
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 head0.035
GPT teacher head0.295
Teacher spread0.260 · 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

Citations12
Published2022
Admission routes3
Has abstractyes

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