Bibliographic record
Abstract
The paucity of information about young impaired driving offenders in the criminal justice system is somewhat surprising given the proportion of youth who continue to be involved in alcohol-related crashes, the important role of age of onset in predicting future criminal behavior, and the fact that impaired driving is one of top five offences committed by young male recidivists. There are important consequences associated with this apparent gap in existing research. Today, in many jurisdictions across Canada and the United States, young impaired drivers are frequently subject to the same traditional sanctions that are applied to adult offenders (e.g., fines, probation, community service, treatment, and incarceration) despite limited evidence of the effectiveness of these strategies even with adults. This has important implications for young impaired drivers and the criminal justice practitioners who process them. Limited knowledge about effective strategies for these offenders has led to inconsistent and possibly ineffective approaches being applied to this population. And, without effective strategies, these young offenders are at risk of becoming tomorrow’s adult drunk drivers who will continue to be involved in the justice system. Based on existing evidence that demonstrates that young impaired drivers pose a greater crash risk to the public on the roadways, and the possibility that these offenders can potentially have longer impaired driving careers, it is important that existing sanctions and programs that are applied to these offenders be evaluated to determine whether they are effective in reducing recidivism, and to guide the development of effective programs to reduce offending among this population.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".