Exploring Workplace Bullying in a Para-Military Organisation (PMO) in the UK: A Qualitative Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".