Bibliographic record
Abstract
Purpose The purpose of this study is to examine the nature of the emotions experienced by targets of bullying in the workplace. Design/methodology/approach A sample of 180 employees in Canada took part in a cross‐sectional self‐report survey study. Findings The study found that, for men, in the presence of belittlement and work being undermined, verbal abuse was negatively associated with confusion, suggesting an active coping strategy. In contrast, for women, in the presence of belittlement and work being undermined, verbal abuse was positively associated with confusion, suggesting a passive coping strategy. Research limitations/implications Although this study's cross‐sectional methodology provided a static snapshot of the emotions of bullying, it may be informative to capture emotions as they arise in response to specific episodes and forms of bullying as well as in response to repeated acts of bullying. Practical implications Workers should be offered resources for understanding and coping constructively with their emotions, training in interpersonal sensitivity to become more aware of and responsive to others' feelings, and the opportunity to work in respectful workplace climates. Originality/value Specific emotions were examined that are associated with exposure to different forms of bullying, and the career‐related implications of these findings are discussed.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".