Collision and Violation Involvement of Drivers Who Use Cellular Telephones
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The study sample consisted of 3,869 drivers, split approximately 50/50 between observed cell phone users and those observed not using cell phones (labeled "nonusers"). Cell phone use was determined by a snapshot observation made on city streets. The sample represented 54% of those originally observed, for whom a match was obtained for both vehicle license plate and for gender and estimated age group of the observed driver and that of the driver named in the vehicle policy. Data were obtained from records of insurance claims, police-reported collisions and violations, following a strict protocol to protect individual privacy. The dependent measures were at-fault crash claims and "inattention" violations. A logistic regression model controlled for age, gender, exposure (represented by not-at-fault crash claims), alcohol-related offenses, and aggressive driving offenses. The study also involved a comparison of the contributing factors and collision configurations of police-reported collisions involving the users and "nonusers" in the sample. Drivers observed using cell phones had a higher risk of an at-fault crash than did the "nonusers," although the difference was not significant for males. There was no apparent effect on "inattention" violations. The cell phone users also had a higher proportion of rear-end collisions. The violation pattern of cell phone users suggests that they are, in general, riskier drivers. These differences likely reflect lifestyle, attitude and personality factors. It is essential to control for these factors in assessing the direct risk attributable to cellular telephone use.
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.
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 | 0.000 | 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 it