Evaluation of Beginner Driver Education in Oregon
Bibliographic record
Abstract
Although driver education (DE) is widely accepted as an effective teen driver safety measure and widely available in the United States, Canada and elsewhere, evaluations have generally failed to show that such formal programs actually produce safer drivers. To address the issue of safety effects as part of a larger investigation, two studies were conducted to examine whether the Oregon Department of Transportation (ODOT)-approved DE program was associated with reductions in collisions and convictions. In the first study, DE status among a relatively small sample of teens who completed an online survey was not found to have a significant effect on collisions and convictions. In the second study, of a much larger population of teen drivers, DE status was associated with a lower incidence of collisions and convictions. On balance, this suggests that the safety effects of DE are either neutral, based on the results of the first Oregon study, or cautiously optimistic based on the results of the second study. The implications of these findings are discussed in terms of making improvements in DE that are evidence-based, and the need for further evaluation to establish that improved and new programs meet their safety objectives.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".