The challenges of implementing interlock best practices in a federal state: the Canadian experience
Bibliographic record
Abstract
Alcohol interlocks have been recognised as an effective and important component of the strategy to deal with impaired drivers.1 ,2 An interlock is a small breath-testing device connected to the engine to prevent a vehicle from being driven if the driver's blood-alcohol concentration (BAC) is above a low preset level (usually 0.02%). Interlocks contain sophisticated anticircumvention features and computerised data logs that record the results of all breath tests and attempts to tamper with the device. Over the past few decades, various jurisdictions have introduced interlock programmes as an impaired driving countermeasure. Accordingly, interlocks have been the subject of extensive consultation and collaboration among researchers and policy-makers worldwide.3 This has resulted in a catalogue of ‘best practices’ that is generally supported by researchers and readily accessible to governments and licensing authorities.4 Nevertheless, there remains a relatively disparate set of interlock programmes in force around the world. The variety of programmes is particularly obvious in federal nations, like Canada, where each province and territoryi has authority over driver and motor vehicle licensing. ### Canada's division of legislative authority Canada is somewhat unique among federal nations in that criminal law is governed by a federal Criminal Code .5 Thus, it is the federal government that has established the criminal impaired driving offences and their respective penalties. For example, the offences of impaired driving, driving with a BAC above 0.08% and refusing to participate in a required impairment test each carry a minimum sentence of a $1000 fine and a 1-year driving prohibition for a first offence. In addition to and alongside these federal criminal sanctions, each province can impose administrative licensing and other countermeasures, including suspensions and mandatory remedial programmes.6 , …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".