Best Practice in Alcohol Ignition Interlock Schemes
Bibliographic record
Abstract
Australia’s National Road Safety Strategy 2011-2020 proposes greater use of alcohol ignition interlocks. To inform a potential expansion of interlock use, an international literature review examined the influence of mandatory versus voluntary alcohol ignition interlock schemes (AIS) in offenders’ subsequent driving and broader rehabilitation, and interlocks as preventative measures in occupational driving contexts. Additionally, the review documented AIS operational effectiveness in relation to first offenders versus repeat offenders, timing of program admittance and exit, program monitoring, participant support programs and problems experienced in AIS implementation. Evaluations of road safety effectiveness for AIS in Canada, USA, Sweden and Australia were also studied. The literature review yielded a substantial list of considered best practice components of effective AIS, ranging from the various broad contexts where interlock use can be usefully encouraged or mandated, down to specific operational considerations. Identifying best practice components affords assistance to any efforts to progress the National Strategy’s vision for the future deployment of interlocks.
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 | 0.074 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".