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Record W2004297249 · doi:10.5539/ibr.v4n2p116

Exploring Workplace Bullying in a Para-Military Organisation (PMO) in the UK: A Qualitative Study

2011· article· en· W2004297249 on OpenAlexvenueno aff
Oluwakemi Owoyemi

Bibliographic record

VenueInternational Business Research · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsnot available
Fundersnot available
KeywordsWorkplace bullyingExploratory researchPsychologyQualitative researchNorm (philosophy)Public relationsSocial psychologyApplied psychologySociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Research into workplace bullying is taking various turns with most of the studies broadening understanding of the concept. Although much progress has been reported in research on the understanding of what is workplace bullying, its effects and how to deal with it. In this paper, exploratory semi-structured interviews were conducted on twenty-five participants to create a better understanding of their experiences of workplace bullying in a para-military organisation in the UK. This method of data collection helped to understand how things happen and why it happened in the para-military organisation. The study revealed that workplace bullying is as a result of organisational change, organisational division into uniformed and non-uniformed staff, power relations, management style and witnessing bullying. The study also revealed that workplace bullying has a detrimental effect on the physical and mental health of the victim. While all the accounts discussed above were given by those who have experienced workplace bullying, the key informants within the organisation gave conflicting account of what is going on in PMO. The findings revealed different views to bullying within the PMO. It may be concluded from this study that bullying is part of the culture of this organisation, and that may be why it is perceived to be accepted as a norm and is continuing.

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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.485
GPT teacher head0.487
Teacher spread0.002 · 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 designQualitative
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

Citations3
Published2011
Admission routes1
Has abstractyes

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