Interlocks for First Offenders: Effective?
Bibliographic record
Abstract
OBJECTIVE: Vehicle interlocks have been shown to effectively reduce the recidivism of multiple driving-while-impaired (DWI) offenders; however, the evidence for their effectiveness with first offenders has been mixed. Two Canadian studies found that the installation of an interlock reduced first DWI recidivism, but U.S. studies in West Virginia and California failed to find a significant reduction in recidivism for first DWI offenders in interlock programs. The objective of this study was to determine the extent to which such devices were effective with first offenders in New Mexico. METHODS: This study compared 1,461 first offenders, who installed interlocks in New Mexico between January 1, 2003, and December 1, 2005, with 17,562 first offenders convicted during the same period who did not install the units. Cox multivariate proportional hazards regression (CMVPHR) was used to compare recidivism rates during three periods: while the interlock was on the vehicles of offenders who installed them, after those offenders removed the units until the end of the study period (approximately 2 years), and for the combined period (both while the interlock was installed and after it was removed). RESULTS: While the device was on the vehicles of the interlock group, their recidivism rate, 2.6% per year of exposure, was significantly less than the 7.1% per year rate of the comparison group (CMVPHR hazard ratio = 0.39, p < 0.0001). After the device was removed, the annualized recidivism rate of the interlock group increased to 4.9% per year of exposure, which was less than the 6.7% rate of the comparison group, but the hazard ratio was not statistically significant (CMVPHR hazard ratio = 0.82, p = 0.16). When the combined periods (interlock on and off) were considered, the interlock group had a recidivism rate of 3.9% per year, which again was significantly lower than the 6.8% rate for the comparison group (CMVPHR hazard ratio = 0.61, p < 0.0001). CONCLUSION: The study provides evidence that interlocks are as effective with first offenders (approximately 60% reduction in recidivism when on the vehicle) as they are for multiple offenders. In addition, the benefits of requiring an interlock for first offenders exceed the costs by a factor of three.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".