Comparative and joint prediction of DUI recidivism from alcohol ignition interlock and driver records.
Bibliographic record
Abstract
OBJECTIVE: This work was conducted to find practical predictors that anticipate which driving under the influence (DUI) offenders will continue to drink and drive after a period of alcohol ignition interlock-controlled driving ends. The interlock prevents impaired driving by requiring a low blood alcohol concentration (BAC) breath sample before allowing an engine to start. Each breath test is recorded. The study evaluated the interlock record as a predictor of future DUI offenses relative to driver records and self-report items. METHOD: Subjects were 2,273 DUI offenders in Alberta, Canada, who used an interlock to gain full reinstatement of driving privileges; for 2,134, the installed periods ranged from 5 to 30 months. A median of 8.1 breath tests was logged for each installed day; 9.9 tests were taken on each day of vehicle use (4.3 starts plus 5.6 running retests). Predictors of postinterlock repeat DUI were compared by sensitivity and survival analyses. RESULTS: Although 69% of all interlock users had at least one BAC test > or = .04% (a "fail" test) within the first 5 months, only 9% were reconvicted up to 4 years after interlock removal. Failed interlock tests proportional to all BAC tests taken was the best predictor of driver recidivism risk during the years following interlock removal. CONCLUSION: The interlock record provides new information, particularly about drivers with no prior DUI offenses. Prior moving violations and driving while suspended convictions, although better predictors than questionnaire data, were poorer than interlock records and prior DUI offenses. The alcohol interlock, already recognized as a useful control device, warrants attention for DUI prediction as well.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".