Young DUI Offenders Seen in Substance Abuse Treatment
Bibliographic record
Abstract
Despite considerable efforts to reduce the burden of driving while under the influence of alcohol or drugs, Driving Under the Influence (DUI) crashes remain a major road safety problem (Chou et al., 2006). While research has demonstrated that apprehended DUI offenders are often a heterogenic group (Begg et al., 2003; Nochajski & Stasiewicz, 2006), young offenders remain an "at risk" group and continue to be disproportionately represented in DUI statistics (Chou et al., 2006; Chirstoffersen et al., in press; Greening & Stoppelbein, 2000; Horwood & Fergusson, 2000). Young men ages 18 to 20 reported DUI more frequently than any other age group (Shults et al., 2002; Quinlan et al., 2005), and not surprisingly, age and DUI have a negative relationship (Chou et al., 2006). Being involved in an alcohol-related crash at a young age does not appear to be a significant deterrent against re-offending, as research has indicated such individuals are in fact more likely to drink and drive as well as crash again in the future (Ferrante et al., 2001). And young males are at a higher risk of engaging in DUI offenses than females (Chou et al., 2006), although an increasing number of females are being apprehended for DUI offenses and entering treatment programs as a result of a DUI (Maxwell et al., 2007).
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".