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Record W3209945087 · doi:10.1108/ijoa-06-2021-2827

Re-positioning workplace aggression interventions: a violence framework

2021· article· en· W3209945087 on OpenAlexaff
Kathy Sanderson

Bibliographic record

VenueInternational journal of organizational analysis · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsLakehead University
Fundersnot available
KeywordsAggressionPsychologySocial psychologyDomestic violenceOriginalityNorm (philosophy)Perspective (graphical)Psychological interventionPower (physics)Poison controlHuman factors and ergonomicsPolitical scienceMedicineCreativity

Abstract

fetched live from OpenAlex

Purpose This paper aims to investigate the socio-psychological systems in organizations that structurally support workplace aggression. Design/methodology/approach Using both a structural and contextual model of intimate partner violence (IPV), the factors supporting workplace aggression were analyzed. The narratives were provided from the participants’ lived experiences of workplace aggression, producing clear indications of where formal and informal power reside. Findings The methods of power and control used by workplace perpetrators parallel those illustrated in IPV. The inaction of management and the lack of social support enabled informal power asymmetries and the organizational norm of silence. The findings have implications for how workplaces view and intervene in relationship-based violence. Originality/value Workplace aggression has been studied from a conflict management perspective, without exploring the components that enable and support organizational abuse. As a result, organizational responses to workplace aggression have failed to address the complex relationship-based components and consequences. The primary contribution of this study is the disruption of the conflict-based perspective of workplace aggression into a more appropriate framework of violence, power and control.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.714
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.018
GPT teacher head0.353
Teacher spread0.335 · 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.

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

Citations4
Published2021
Admission routes1
Has abstractyes

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