A Data-Based Assessment of the Impact of Marijuana Legalization on Vehicle Accident Experience
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
Recreational use of marijuana has been legalized recently in many areas in North America. One of the effects that interests insurance companies is the change in vehicle accident experience. This study summarizes information on the car accident experience in Canada and several US states and provides robust estimates of the legalization impacts based on recent methodological developments for the analysis of observational data, including machine learning and other data-driven techniques. The study did not detect statistically significant impacts of legalization on the car accident fatality rate, insurance claim frequency, or average cost per claim. The estimated seasonality and pre-legalization dynamics in Canadian vehicle insurance statistics continued after legalization without a significant change. In the U.S., temporal patterns of human activity (such as yearly, weekly, and daily cycles) and inclement weather are much better predictors of the vehicle accident experience than marijuana legalization. Address for Correspondence: lyubchich@umces.edu
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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 | 0.001 |
| 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