Bibliographic record
Abstract
Purpose This paper seeks to theorize the interpretations and reactions of targets and witnesses to subtle forms of bullying. Design/methodology/approach A theoretical approach was used to understand target and witness interpretations and reactions. Learned helplessness theory and social influence theory are drawn upon. Findings This paper revealed that subtle forms of bullying behaviors will be more likely to induce confusion from both targets and witnesses. Targets will tend to be more confused in response to subtle bullying and attribute environmental factors for the behaviors. This will decrease their likelihood to react against the bullying. Witnesses will also experience greater confusion and will tend to side with the perpetrator, particularly when the perpetrator is an important organizational member (e.g. supervisor). Witnesses may internalize the behaviors, leading to greater permeability of the bullying through the organization. Originality/value This paper sheds light on two important and under‐researched aspects of workplace bullying, i.e. subtle bullying behaviors and witnesses of bullying. This paper counter‐intuitively suggests that subtle bullying behaviors may in fact be more harmful to targets than explicit bullying behaviors. Also, witnesses may represent a “dark side” of bullying in which they enable the bullying to be increasingly difficult to defend against. This contributes to our understanding of the intensification of bullying.
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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".