Explaining the Frequency and Variety of Crimes through the Interaction of Individual and Contextual Risk Factors
Bibliographic record
Abstract
This study explored the explanatory power of the interaction model between individual and contextual risk, in comparison to the additive model, to explain delinquency. It was conducted with 235 offenders, who completed self-report questionnaires regarding antisocial traits and attitudes, criminal entourage, lifestyle, and delinquency. Multiple linear regression analyses (additive combination) and regression trees (interaction combination) were produced. In general, the factors favouring the formation of criminogenic situations (personal characteristics, criminal entourage, and deviant lifestyle) all significantly contributed to the explanation of the frequency and variety of crimes. However, the regression tree results suggested that it is necessary to understand the level of both individual and contextual risk to adequately explain delinquency. Our results suggest abandoning the additive approach currently used in the assessment of recidivism risk in favour of an interactional approach because it better reflects reality.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".