The “Normalization” of Deviance: A Case Study on the Process Underlying the Adoption of Deviant Behavior
Bibliographic record
Abstract
SUMMARY In 2011, the Québec government launched the Charbonneau Commission, a monumental public inquiry tasked with getting to the bottom of a major collusion and corruption scandal involving elected officials, municipal employees, and construction industry contractors. The scandal concerned the awarding of municipal contracts that led to a significant waste of public funds. Yet, at the time, all municipal sector organizations involved were regulated by several controls, particularly in the granting of public contracts. How could the situation deteriorate to the point where collusion became the “usual” way of managing public contracts, particularly in the City of Montréal? The objective of this article is to better understand how deviance became the “norm,” such that the social actors involved came to adopt a deviant identity rather than obeying socially accepted rules. Conceptually inspired by the work of Becker (1963), Foucault (1977), and Giddens (1991), this article is based on the in-depth testimonies of two key actors in the collusion scheme. Our aim is to better understand the process leading to the adoption of deviant behavior and the often illusory character of organizational and regulatory controls.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| 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".