Supervisory Level and the Impulse to Harm a Coworker: Advancing a Bourdieusian Perspective
Bibliographic record
Abstract
Abstract Purpose The malicious impulse is a phenomenon that lies in the theoretical and ontological space between emotion and action. In this chapter, we probe this space. In the empirical part of this work, we evaluate the hypothesis that middle-level supervisors will be more likely than non-supervisory workers and top-level supervisors to report an impulse to “hurt someone you work with” (i.e., maliciousness). Methodology/approach Data are from a cross-sectional survey of a representative sample of employed Toronto residents in 2004–2005. Findings Results from logistic regression analyses show that when job characteristics are controlled, the estimated difference between middle-level supervisors and workers in other hierarchical positions reporting the impulse to harm a coworker is statistically significant. Moreover, the difference between middle-level supervisors and other workers persist after controls for anger about work and job-related stress. Social Implications In discussing our results, we focus on factors that might generate the observed associations, and on how Bourdieusian theory may be used to interpret the social patterning of impulses in general, and malicious impulses in particular. We also discuss the implications of our findings for emotional intelligence in the workplace.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".