The Need for Drugged Driving<i>Per Se</i>Laws: A Commentary
Bibliographic record
Abstract
OBJECTIVE: Triggered by the new federal commitment announced by the Office of National Drug Control Policy (ONCDP) to encourage states to enact drugged driving per se laws, this article reviews the reasons to establish such laws and the issues that may arise when trying to enforce them. METHODS: A review of the state of drunk driving per se laws and their implications for drugged driving is presented, with a review of impaired driving enforcement procedures and drug testing technology. RESULTS: Currently, enforcement of drugged driving laws is an adjunct to the enforcement of laws regarding alcohol impairment. Drivers are apprehended when showing signs of alcohol intoxication and only in the relatively few cases where the blood alcohol concentration of the arrested driver does not account for the observed behavior is the possibility of drug impairment pursued. In most states, the term impaired driving covers both alcohol and drug impairment; thus, driver conviction records may not distinguish between the two different sources of impairment. As a result, enforcement statistics do not reflect the prevalence of drugged driving. CONCLUSIONS: Based on the analysis presented, this article recommends a number of steps that can be taken to evaluate current drugged driving enforcement procedures and to move toward the enactment of drug per se laws.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".