PROPOSITION 8 AND CRIME RATES IN CALIFORNIA: THE CASE OF THE DISAPPEARING DETERRENT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research Summary: In 1999, Daniel Kessler and Steven Levitt published an article that purported to provide support for the marginal deterrent effects of harsher sanctions on levels of crime. Specifically, they concluded that sentence enhancements that came into effect in California in June 1982 as a result of Proposition 8 were responsible for a subsequent drop in serious crime in this state. Our article examines the analyses and findings of this article and suggests that their conclusion of a deterrent impact fails to withstand scrutiny when more complete and more detailed crime data are used and the comparability of “control” groups is carefully examined. In particular, the addition of annual crime levels for all years (versus only the odd‐numbered years that Kessler and Levitt examine) calls into question the prima facie support for a deterrent effect presented by Kessler and Levitt. Specifically, it demonstrates not only that the crime drop in California began before, rather than after, the passing into law of the sentence enhancements in 1982 but also that the downward slope did not accelerate after the change in law. Furthermore, the comparability of the two “control” groups with the “treatment” group is challenged, rendering suspect any findings based on these comparisons. Policy Implications: Case studies suggesting that crime decreased after the imposition of harsh sentencing policies are often cited as evidence of marginal general deterrence. As has been demonstrated in other contexts, the question that needs to be asked is “Compared with what?” Kessler and Levitt's (1999) article demonstrates that those interested in sentencing policy need to be sensitive not only to the appropriateness of the comparisons that are made, but also to the choice of data that are presented.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it