Upwards Workplace Bullying: A Literature Review
Bibliographic record
Abstract
There is a large body of international literature on most aspects of workplace bullying, yet there are only a few research articles dealing with bullying of bosses, managers, and leaders by their subordinates and staff members. Over time the term “upwards bullying” has been accepted as the generic term to describe this phenomenon but using these search terms does not provide a comprehensive indication of relevant publications. This literature review identifies and collates English language research on upwards bullying to document its research status, its lack of visibility in the workplace, and to connect upwards bullying with related research on aggression against supervisors and managers. Included is research that specifically labels the phenomena as upwards or upward bullying, along with related terms of subordinate-initiated bullying, supervisor-targeted bullying, bottom-up bullying, bullying the manager, and bullying the boss. Nonacademic publications are not included, nor is research on related workplace misbehavior such as cyberbullying, legal action resulting from upwards bullying or whistle blowers, and extortion or blackmail. Areas for further exploration in the field are identified with indicators of how this type of workplace bullying differs from lateral and downward bullying. Coverage up to the year 2020 is provided with the expectation that the trend of working from home driven by safety considerations during the COVID-19 epidemic could change employee responses and reactions to their workplace leaders and management.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".