CRIMINAL PROFILES OF DRINKING DRIVERS IN ONTARIO
Bibliographic record
Abstract
The paper reviews the criminal and driving history of a sub-sample of 100 drivers drawn randomly from 879 drivers charged with an alcohol related driving offence in Toronto, Ontario, Canada in 1996. Many of the drivers had previous unsafe driving ranging from prior drinking and driving convictions to careless driving convictions and unsafe driving behaviours. Some of the drivers identified as first time offenders under the 1996 laws were actually repeat offenders. The data also suggest drivers with a BAC in excess of 120mg are more likely to be convicted of a drinking and driving related criminal offence. The data also suggest that some improvement is required in communicating charges and convictions to both the criminal and transportation databases so that complete, up to date records are ensured resulting in the appropriate treatment and sanctioning of convicted drivers. More analysis of a similar 1998 file will be required to further assess some of these issues and monitor changes in the criminal law and related provincial laws in the intervening time period. For the covering abstract see ITRD E106992.
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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) | 0.007 | 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".