Darwin Meets the King: Blending Sociology and Evolutionary Psychology to Explain Police Deviance
Bibliographic record
Abstract
Ce papier utilise la perspective de Parnaby et Leyden's ( ) qui adapte l'approche de Merton de la déviance policière en incorporant des éléments de psychologie évolutionniste. Nous proposons que l'intégration de la psychologie évolutionniste contribue à un modèle théorique plus robuste. Plus précisément, nous considérons qu'une structure sociale anomique crée les conditions sociales par lesquelles des mécanismes psychologiques adaptifs qui coïncident avec les modes d'adaptation de Merton et se manifestent dans le comportement. Nous soulignons l'importance d'une intégration de la sociologie et de la psychologie évolutionniste et adressons certains malentendus sur cette dernière. In this paper, we build on Parnaby and Leyden's ( ) modified Mertonian approach to police deviance by incorporating elements of evolutionary psychology. It is our contention that an infusion of evolutionary psychology will lead to a more robust theoretical model. Specifically, we argue that an anomic social structure helps create the social conditions within which specific adaptive psychological mechanisms manifest behaviorally in ways that coincide with Merton's modes of adaptation. The importance of integrating sociology and evolutionary psychology is emphasized while a number of misunderstandings about the latter are addressed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".