Assessing Risk of Victimization through Epidemiological Concepts: An Alternative Analytic Strategy Applied to Routine Activities Theory*
Bibliographic record
Abstract
Cet article fait appel aux concepts et aux techniques de l'épidémiologie pour examiner la capacité de la théorie des activités routinières à expliquer le risque de victimisation criminelle. En allant au-delà de l'identification des facteurs de risque de victimisation, les auteurs se demandent comment les changements des facteurs de causalité pourraient influer sur ce risque dans la population générale. lis trouvent que les prédicteurs établis avec des méthodes plus traditionnelles expliquent la plus grande partie du risque, mais que certains sont moins importants pour la compréhension du risque de la population dans l'ensemble en raison du petit nombre de personnes qui leur est associé, tandis que d'autres sont plus utiles parce qu'ils s'appliquent à un plus grand nombre de personnes. This paper draws upon concepts and techniques from epidemiology to examine the ability of routine activities theory to account for the risk of criminal victimization. Moving beyond the identification of risk factors for victimization, we ask how changes to causal factors might affect the risk of victimization in the general population. We find that predictors identified with more traditional methods account for the bulk of the risk, but that some are less important for understanding overall population risk because of the small numbers of people associated with them, while others are more helpful because they apply to larger numbers.
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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 | 0.023 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".