Abusive Supervision and Supervisor-Directed Deviance: A Social Network Approach
Bibliographic record
Abstract
Supervisor-directed deviance is a well-established consequence of abusive supervision. However, prior accounts of the abuse–deviance relationship have overlooked the role played by power embedded in subordinates’ informal social context. To address this gap, we draw on power-dependence theory and use a social network approach to explain the link between abusive supervision and supervisor-directed deviance. In doing so, we propose a three-way interaction in which the abuse–deviance relationship is impacted by two components of informal power: subordinate social network centrality and subordinate influence. In particular, we propose that the relationship will be the strongest when subordinates have high betweenness centrality and high influence. We gathered full social network data, as well as self-report surveys from 272 primary school teachers and government contract workers in Northern China. Our results provide support for the notion that supervisor-directed deviance emerges most strongly as a consequence of abusive supervision for employees who wield informal power in their organization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".