Understanding the Distribution of Crime Victimization Using “British Crime Survey” Data
Bibliographic record
Abstract
Abstract This chapter focuses upon understanding the data-generating process that gives rise to the frequency distribution of crime victimization count data sampled from the British Crime Survey (BCS). Having described the form of this data, it introduces the problem of explaining the overdispersion of the distribution. It discusses and compares various models of the data that make assumptions about the data-generation process, including risk-heterogeneity, state-dependency, conditional probability, the double hurdle model, and the spells model. Its principal conclusion is that the crime victimization frequency distribution found in BCS data is the heterogeneous product of the mixing of two probability distributions: one concerns victim-prevalence, where zero-inflation predominates; the other concerns victimization-frequency, expressing the long-tail of high-frequencies. This conceptualization sheds new light on some persistent difficulties that might point the way towards future progress in understanding and remedying the distribution of crime victimization among citizens.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".