Do Thugs Sell? An Investigation of the Relationship between Deviance and Stakeholder Response
Bibliographic record
Abstract
Although there have been many cases in the popular press detailing the deviant actions of celebrities, athletes, and corporations, theoretically, little is known about the relationship between an individual’s deviant actions and the response from key stakeholders that support that individual. On the one hand, the concept of deviance or misconduct is fuzzy (Vaughan, 1999). Similarly, it is not so straightforward that key stakeholders will always sanction an actor that is deviant (Bensman & Gerver, 1963). Drawing upon an empirical analysis of the relationship between misconduct by professional football players, and the influence that this has on attendance at football games, we examine the relationship between deviance and stakeholder response. Additional, we build upon Greve, Palmer and Pozner (2010) theoretical insights on social-control agents to examine if the relationship between deviance and stakeholder response is moderated by a change in social-control agents. Our study speaks to the renewed interest in stakeholders (Barnett, 2012) and the link between deviant actions and organizational outcomes Greve, Palmer and Pozner (2010).
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".