Controlling Impaired Driving Through Vehicle Programs: An Overview
Bibliographic record
Abstract
The growing recognition of the problem presented by illicit vehicle operation by those whose license has been suspended for driving while intoxicated (DWI) has led to the increasing use of vehicle sanctions. These sanctions include vehicle impoundment and forfeiture, vehicle registration cancellation, and vehicle interlocks as penalties for DWI and driving while suspended (DWS). This article reviews the current information available on the use and effectiveness of vehicle sanctions for reducing offender recidivism. In the United States, 14 states have impoundment laws that are widely used as sanctions for both DWI and DWS, with the length of the impoundment increasing with the number of previous offenses. These laws have been shown to reduce recidivism while the vehicle is in custody and, to a lesser extent, even after the vehicle has been released. Vehicle impoundment is also widely used in Canada and New Zealand. Although a larger number of U.S. states have laws providing for vehicle forfeiture for DWI or DWS, this sanction tends to be limited to multiple offenders and therefore impacts fewer drivers. Cancellation of the vehicle registration and the confiscation of the vehicle plates are increasing in popularity because the vehicle tags are the property of the state, rather than the vehicle owner. Vehicle alcohol interlocks have proven to be an effective method for reducing DWI offender recidivism while they are on the car, but appear to produce only limited post-treatment behavior change. Interlocks are widely used in the United States and Canada and are beginning to be implemented in Europe and Australia. The issues that arise in implementing vehicle sanction programs are discussed and the actions taken by states to deal with them are described.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".