Bibliographic record
Abstract
Driving-under-the-influence (DUI) recidivists pose a unique problem in road safety because of their apparent inability to respond to the preventative measures that have been put in place to ensure road safety. This relatively small sub-group of drivers cause a disproportionably large number of victims. The majority of offenders are men - 95% in a Quebec study (Bergeron et al, in process). As a group, they have been punished, quite severely in many instances, and still use their car when drunk in spite of the understanding that a second or third offence involves more severe punishment than the previous one(s). Alcohol ignition interlock recorders have significantly reduced recidivism for the minority of offenders who have been willing to participate but outcome studies show that once the device is removed, the rate of DUI is comparable to those offenders who refused to participate (Voas et al, 1999, SAAQ, unpublished data). DUI can be put to a halt by environmental contingencies, but the intention to relapse appears to remain unaffected. In the language of motivation, motivation remains extrinsic (the behavior is performed because of external constraints), it never becomes intrinsic (an integral part of the person's behavioural repertoire). This lack of conscience and ongoing disrespect of the social consensus constitute one of the most significant challenges in road safety today. (A) For the covering abstract of the conference, see ITRD Abstract No. E201067.
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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".