Bibliographic record
Abstract
This paper describes the ignition interlock program, which is one component of a broader strategy that deals with drinking and driving. This broader strategy involves a variety of measures that include: administrative driver’s license suspensions (ADLS), extended mandatory suspension periods, and a remedial measures program. Other components of this strategy include: (1) a 12-hour roadside driver license suspension for a blood alcohol concentration (BAC) range from .05 to.08, (2) vehicle impoundment for drivers caught driving with a license suspended for a Criminal Code of Canada violation, and (3) dedicated funding for random spot check programs, i.e., R.I.D.E. (Reduce Impaired Driving Everywhere). The paper describes how Ontario is the largest jurisdiction in Canada, by both driver population and number of vehicles, numbering 8.3 million drivers and 9.4 million vehicles. It is also worth noting that Ontario averages approximately 16,000 convictions for impaired driving annually. Alcohol-related driving collisions and the fatalities associated with these collisions have been dramatically reduced over the last ten years as a result of the cumulative effect of the programs that Ontario has introduced over the years.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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".