Bibliographic record
Abstract
In anticipation of federal legalization of cannabis in Canada, Nathan Baker provides an excellent overview and a detailed account of how the federal and provincial governments propose to detect, investigate, and prosecute drug-impaired driving to ensure the safety of the public on its roads. Drug-impairment tests, such as Drug Recognition Evaluations (DRE) and Standardized Field Sobriety Testing (SFST) have been statutorily embedded in our criminal justice system for over ten years. However, the need for heightening awareness of these testing procedures, training for police officers who administer these tests, and education on the accuracy, validity, and credibility of drug detection technologies has been brought to the forefront of criminal law discussions as a result of legalized cannabis use. The author’s goal is to “provide a better understanding of the science and how it should relate to the creation and interpretation of the law in Canada surrounding impaired operation by drugs.” As such, Baker provides in-depth detail surrounding the efficacy of drug-impairment testing.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.010 |
| 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".