Chronic monitoring for wrongdoing as a signal of immoral character
Bibliographic record
Abstract
• We introduce chronic monitoring for wrongdoing as a novel factor influencing the reputation of third-party actors. • Third-party punishment is often seen as less moral when it is preceded by chronic monitoring for wrongdoing. • Chronic monitoring for wrongdoing signals competitive-leveling motives and a tendency to ascribe hostile intentions to others. • We ground our research in scholarship explaining why third-party punishment signals cooperative intent. Punishing wrongdoing can sometimes have reputational benefits. But what do people think of those who regularly monitor their environment for signs of wrongdoing? Drawing on the concept of workplace vigilantism, we posit that acts of monitoring in workplace settings serve as negative cues of one’s moral character. In particular, we propose that chronically monitoring for signs of wrongdoing signals that an individual is driven by retributive and competitive leveling motives as well as a tendency to ascribe hostile motives to others. We examine this idea across six studies (and three supplementary studies). In Study 1, we find that employees have largely negative impressions of individuals who vigilantly monitor and reprimand wrongdoings at work. In Study 2, we find that punishers are seen as less moral when their acts of punishment are preceded by chronic monitoring for wrongdoing. In Study 3, we find that punishers who engage in chronic monitoring are seen as possessing heightened retributive and competitive leveling motives. In Study 4, we find that the reputational costs of chronic monitoring persist even when the violation is addressed in a courteous manner and that chronic monitoring signals that one ascribes hostile intentions to others. In Study 5, we identify an individual difference moderator, showing that negative judgments of workplace vigilantes are attenuated when observers share similar vigilante tendencies. Finally, in Study 6, we find that the reputational costs that result from chronic monitoring are observed across an array of workplace violations, including when the violation is of considerable organizational importance. Together, our results demonstrate that the perceived moral character of a punisher can hinge on whether monitoring for wrongdoing precedes such punitive acts.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".