Estimating the True Rate of Repeat Victimization from Police Recorded Crime Data: A Study of Burglary in Metro Vancouver
Bibliographic record
Abstract
Predictive policing seeks to allocate scarce resources where and when they are most needed. Yet analysis is often based on recorded crime data that typically understate the concentration of crime at the same places or against the same people. This study outlines a means of developing more accurate estimates, termed the Recorded Repeats Adjustment Calculator (RRAC), and applies it to burglary data for Metro Vancouver. Whereas repeat burglaries constituted 20% of recorded burglaries, after adjustment they were half of burglaries. Moreover, households with five or more burglaries accounted for less than 1% of recorded but, after adjustment, one in five actual burglaries (21%). These results are closer to those found by crime victim surveys but still likely to be conservative. The study aspires to produce a tool for analysts that produces more accurate information on crime's concentration and, thereby, more informed crime control efforts.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".