What Will Happen to the Teen Drivers of Today? A Triage of Research and Intervention Issues
Bibliographic record
Abstract
The purpose of this panel session is to reflect on and debate the advances and challenges associated with driving as a teen. Traffic crashes are the leading cause of death in the U.S. and worldwide in this age group. What research has contributed to our understanding of this state of affairs and what research and interventions are still needed? A group of internationally known researchers will present research on the contributions of driving behavior (e.g. secondary task engagement) and driving conditions (e.g. teenage passengers) on teen crash risk and the potential for interventions, such as education, training, and graduated drivers licensing (GDL) to reduce this risk. The breadth and depth of the panelists’ knowledge will be tested by audience questions and directed by the provocateur as a range of additional possible contributions and countermeasures are considered and prioritized.
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 | 0.054 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.026 | 0.037 |
| Insufficient payload (model declined to judge) | 0.010 | 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".