Getting Away with It (Or Not): The Social Control of Organizational Deviance
Bibliographic record
Abstract
The phenomenon of organizations breaking laws and norms in the pursuit of strategic advantage has received substantial attention in recent years. Such transgressions generally elicit the intervention of social control agents seeking to curb deviant behavior and defend the status quo. In some cases, their efforts result in the deviant behavior being suppressed; in other occasions, however, organizational deviance can persist and even be accepted into the very system of rules that was initially challenged. In this paper, we advance a structured view of this process by formulating a theory of the social control of organizational deviance. Building upon the sociological literature, we classify forms of social control based on their cooperativeness and formality; additionally, we shed light on the outcomes of social control by illustrating the conditions under which they are likely to be more or less accommodative of deviant behavior, as well as more or less permanent. In so doing, we contribute to the scholarly understanding of the role of social control in organizational fields, as well as of the advantageousness of deviant behavior as a strategic option for organizations.
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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 | 0.011 |
| 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 | 0.015 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".