Par-delà le sacrifice nécessaire et le tueur absent : l’importance d’étudier les crimes en col blanc sous l’angle de crimes systémiques
Bibliographic record
Abstract
Les crimes en col blanc couvrent un large registre d’infractions et de conduites, et si certaines de ces conduites ne sont pas très graves (des infractions techniques, précisément le non-respect de règlements), d’autres mettent en péril de vastes pans de la population et à ce titre semblent relever plus volontiers de procédures pénales. De manière à souligner ce caractère paradoxal des crimes en col blanc, nous revisitons, notamment en mobilisant deux théories contemporaines (le sacrifice nécessaire et le tueur absent), des cas qui ont marqué l’imaginaire, tant les conséquences pour la vie humaine et les écosystèmes dépassent l’entende-ment. Dans un tel contexte, il apparaît opportun d’envisager l’étude des crimes en col blanc sous l’angle de crimes systémiques : soit des actions organisées et récurrentes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".