Did Ontario's Zero Tolerance & Graduated Licensing Law reduce youth drunk driving?
Bibliographic record
Abstract
Abstract On April 1, 1994, Ontario, Canada, instituted a new graduated driver license (GDL) system that effectively set the legal blood alcohol content (BAC) threshold at zero for the first few years of a youth's driving eligibility. I use data from the 1983–2001 Ontario Student Drug Use Surveys (OSDUS) to examine whether the Zero Tolerance (ZT) policy reduced self‐reported drinking and alcohol‐involved driving among youth. I find that rates of drunk driving reported by 16‐ to 17‐year‐olds—who faced new, lower legal limits after adoption of the ZT policy—were about 5 percentage points lower after the law was implemented. Visual inspection of the data, however, shows that the estimated reduction is an artifact of a pre‐existing trend: Drunk driving rates in this age group were falling steadily throughout the 1980s and into the 1990s. Estimates that account for this pre‐existing trend or that consider shorter windows around the 1994 implementation date return effects on alcohol‐involved driving that are either small and statistically insignificant or large and implausibly signed (positive). These null findings are robust to using the associated change in outcomes for slightly younger (14–15) or slightly older (19–20) youths as controls in a difference‐in‐differences framework. I similarly find no robust effect on drinking participation. This suggests that Ontario's age‐targeted drunk driving law—despite being harsher than similar policies in the United States—was not responsible for reductions in Canadian youth road fatalities over the past two decades. © 2006 by the Association for Public Policy Analysis and Management
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".