Bibliographic record
Abstract
The legalization of Cannabis has important implications for the life of Canadians including community mobility, law enforcement, and injury prevention, among others. In this context, and at the intersection between these dimensions of civic participation and public health, impaired driving emerges as a concern among the general public, and a risk for Canadian drivers and road users. The scientific community and government agencies have recognized a general need to build a body of evidence around cannabis-related research. However, common pitfalls to the generation of timely, suitable, and effective research must be avoided. This commentary presents a reflection on the role research must play in the development of proactive and pre-emptive action and applies it to the field of impaired driving. The latter is achieved by drawing on the example of alcohol-related research as a blueprint on the path to injury prevention in the context of cannabis-impaired driving.
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 | 0.082 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.019 | 0.034 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".