A comprehensive estimation of costs of crime in South Africa and its implications for effective policy making
Bibliographic record
Abstract
Despite South African crime rates (including homicide) are among the world's top, no comprehensive estimation of criminal costs is being attempted thus far. The increasing attention to estimates of crime-related disability adjusted life years (DALYs) is welcome but we show by estimating the cost of multiple offenses in South Africa (from housebreaking to personal, vehicle and cattle theft, among others) that concentrating the measurement of criminal costs on few items may mislead policy choices. In fact, DALYs associated costs represent less than a quarter of total crime costs after including other medical, institutional, private security, economic costs and transfers (totalling 7.8% of GDP). We conclude that estimating the burden of crime is interesting in itself, but from a policy point of view it is the distribution of this burden across crime categories and cost items that matters. Not only policy making against crime must be evidence-based, but also the generation of information on crime must be also consistent with policy options shown to be effective. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".