Bibliographic record
Abstract
Abstract Engagement in criminal activity may be viewed as risk-taking behaviour as it has both benefits and drawbacks that are probabilistic. In two studies, we examined how individuals' risk perceptions can inform our understanding of their intentions to engage in criminal activity. Study 1 measured youths' perceptions of the value and probability of the benefits and drawbacks of engaging in three common crimes (i.e. shoplifting, forgery, and buying illegal drugs), and examined how well these perceptions predicted youths' forecasted engagement in these crimes, controlling for their past engagement. We found that intentions to engage in criminal activity were best predicted by the perceived value of the benefits that may be obtained, irrespective of their probabilities or the drawbacks that may also be incurred. Study 2 specified the benefit and drawback that youth thought about and examined another crime (i.e. drinking and driving). The findings of Study 1 were replicated under these conditions. The present research supports a limited rationality perspective on criminal intentions, and can have implications for crime prevention/intervention strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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; both teacher heads agree on what is shown here.
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".