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Record W1510915057 · doi:10.1111/joop.12129

Institutional‐level bullying: Exploring workplace bullying during union organizing drives

2015· article· en· W1510915057 on OpenAlexaffabout
Al‐Karim Samnani, Janet A. Boekhorst, J.A. Harrison

Bibliographic record

VenueJournal of Occupational and Organizational Psychology · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsYork UniversityUniversity of Windsor
Fundersnot available
KeywordsWorkplace bullyingHostilityContext (archaeology)PsychologySocial psychologyPublic relationsPolitical science

Abstract

fetched live from OpenAlex

We investigated bullying behaviour within the context of trade union organizing drives. We conducted an archival search using the Ontario Labour Relations Board database, which yielded 146 cases over a 10‐year period. We found that the three core features of workplace bullying were present in 20 of these cases. Through a qualitative content analysis, we found a number of instances in which managers were reported to have repeatedly and persistently engaged in acts of bullying towards union organizers and supporters, intensified the hostility of these acts over time, and exploited their power through threats to job security and captive audience meetings. We also shed light on the role of observers of bullying behaviour and how observers may have distanced themselves from or concealed contact with the target. Practitioner points Managers in organizations may resort to bullying behaviour during the organizing drive, which can produce fear among employees and result in significant legal consequences. Bullying tends to start with subtle and indirect behaviours, but often escalates into more intense forms of bullying. Senior management should ensure that managers and other employees in the organization understand the potential repercussions of their bullying behaviours.

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 score0.032
Threshold uncertainty score0.649

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 communication0.0000.001
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 head0.182
GPT teacher head0.377
Teacher spread0.195 · 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

Citations17
Published2015
Admission routes2
Has abstractyes

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