Comment on Estimating the True Rate of Repeat Victimization from Police-Recorded Crime Data
Bibliographic record
Abstract
The Recorded Repeats Adjustment Calculator (RRAC) methodology introduced by Frank, Brantingham, and Farrell (2012) doesn’t consider the entire probability mass function associated with the binomial distribution. Although the authors recognized and documented this as a limitation, the implications are significant because some of their key findings are not internally consistent with their explicit underlying assumptions or the available empirical data. This article proposes revised estimates, based on a Bayesian treatment of the empirical data and underlying assumptions stated by the authors themselves. According to the initial RRAC estimates they reported, repeat burglary victims would have represented 47.1% of all burglaries or 22.0% of all burglary victims. The proposed revisions suggest instead that repeat burglary victims represent approximately 30.8% of all burglaries or 15.9% of all burglary victims. By comparison, based strictly on the police-recorded data, repeat victims account for 19.8% of all reported burglaries or 10.0% of all burglary victims. While these findings tend to temper the empirical findings and original conclusions of Frank, Brantingham, and Farrell (2012), they provide renewed support for their insight that raw police-recorded crime data are likely to underestimate the true rate of repeat victimization.
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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 | 0.135 | 0.570 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".