A COMPARISON OF DRIVING WHILE INTOXICATED LAWS IN THE UNITED STATES AND CANADA
Bibliographic record
Abstract
This paper examines the similarities and differences in driving while impaired (DWI) laws in the United States (US) and Canada. Essentially all DWI law in the US is governed by state statutes, while many Canadian policies are a part of the Canadian Criminal Code, set by the national Parliament. The federal government in the US has attempted to use financial incentives in order to encourage states to pass more stringent DWI laws, and in particular, a blood alcohol concentration (BAC) limit for DWI of 80 mg%; open container laws, and more severe sanctions for repeat DWI offenders. Legislative records and history have been reviewed in order to determine dates of passage of laws as well as introductions of policy that failed to become law. Records include text of legislation as well as oral and written testimony. A comparison of the dates of enactment of laws in US states and Canadian provinces was made for BAC limits, administrative per se laws, special BAC limits for young drivers, and sanctions for DWI convictions. Federal legislation that provides incentives for passage of state laws in the US has not resulted in across the board passage of .08 or open container laws. Canadian provinces have uniformly lower BAC limits, and stricter penalties for DWI violations. (A) For the covering abstract of the conference, see ITRD Abstract No. E201067.
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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 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".