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
A patrolling police officer is in a more technologically-complex driving environment than a passenger-vehicle driver, and thus is subject to more distraction sources. Although technology-based distractions appear to be a concern for police drivers, the effects of distractions on police-involved crashes have not been empirically studied before. In this study, injury severity in police-involved crashes under varying types of distractions is estimated by an ordered logit model. The model was built on a national crash database: U.S. General Estimates System (2002 to 2008). The results of the model reveal that, given that a crash has occurred, police involvement increases the odds of more-severe injuries. In general, in-vehicle distractions are associated with a higher likelihood of severe injuries. This effect is more profound for police-involved crashes, as the odds of severe injuries increase by almost three fold (odds ratio: 2.82). Cognitive distractions were also found to increase injury severity when the distracted driver was a police, whereas the opposite effect was observed for civilian crashes.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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