Increasing alcohol interlock participation rates in Canada: best practices and the effects of insurance
Bibliographic record
Abstract
Interlocks are an effective means of reducing recidivism among impaired driving offenders. However, interlock programs have suffered from low participation rates, and thus have not achieved their traffic safety potential. Moreover, many offenders who do not participate choose instead to drive without a licence or insurance. These drivers are overrepresented in fatal crashes and expose the public to the risk of uncompensated losses and injuries. This research reviews the alcohol interlock programs across Canada and identifies possible barriers to participation. Having determined the provinces with the highest participation rates, this paper identifies the program features that are most conducive to increasing participation. The provinces with the highest interlock participation rates generally have the most inclusive mandatory programs that apply to the greatest number of offenders. In these jurisdictions, enrollment in the interlock program is a condition of relicensing, preventing offenders from simply lwaiting outr the hard licence suspension period. Most of these provinces also reduce the minimum licence suspension period to encourage offenders to install an interlock. The provinces should design interlock and related policies to maximize participation rates. This includes making participation mandatory for all federal impaired driving offenders and shortening the provincial suspensions that otherwise apply. The total costs of an impaired driving conviction, including insurance, should be kept at a level that does not encourage offenders to forego the interlock program and drop out of the licensing system altogether.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".