Organizational Wrongdoing and its Deterrence
Bibliographic record
Abstract
Strategic management research around organizational wrongdoing has made considerable progress in elucidating how these practices spread both within and across organizations. Central to much of this literature is the logic, famously articulated by Becker (1968), that wrongdoing follows from rational calculation weighing the perceived upsides of these actions against perceived downsides. Integrating perspectives from strategic management, management scholars have enriched this perspective, illustrating how wrongdoing fits into firms’ broader efforts to enhance performance. Naturally, this work has inspired corresponding research on deterrence, wherein greater attention to the strategic motivations for organizational wrongdoing informs novel theories regarding how it may be preempted or cauterized. This symposium brings together scholars whose work is at the cutting edge of these questions. It showcases work illustrating novel motivations for wrongdoing, as well as novel explanations for how and why certain deterrence strategies may prove especially effective. By doing this, we believe this symposium will enhance our understanding on corporate wrongdoing and how it can be more effectively deterred. Dominant Deceptions: Explaining the Tenacity of Deceit in Entrepreneurial Ventures Author: Aharon Cohen Mohliver; London Business School When Punishment Deters Future Misconduct: Evidence from Doping in Cycling Teams Author: Alessandro Piazza; Rice U. Beyond Deterrence: Unintended Consequences of Punishment in the Chicago Police Department Author: Sarah Gordon; McGill U. - Desautels Faculty of Management High-Profile Enforcement Efficiently Deters White-Collar Crime Author: Jin Hyung Kim; George Washington U.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".