How Many Ways Do Offenders Travel -- Evaluating the Activity Paths of Offenders
Bibliographic record
Abstract
According to the Journey to Crime theory, offenders have a directionality preference, in the form of an activity path, when they are moving about in their environment in search for criminal activities. Using clustering techniques, this theory is tested using crime data for the Province of British Columbia, Canada. The activities of 57,962 offenders who were either charged, chargeable, or for whom charges were recommended were analyzed by mapping their offense locations with respect to their home locations to determine directionality. Once directionality was established, a unique clustering technique, based on K-Means clustering and modified for angles, was applied to find the number of activity paths for each offender. Although the number of activity paths varies from individual to individual, the aggregate pattern was very consistent with theory. It was found that people only have a few activity paths, even if they are highly prolific offenders.
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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 | 0.001 | 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) | 0.002 | 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 teacher head, 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".