Bibliographic record
Abstract
Canada is currently considering legislation that will detail the investigative steps necessary to pursue a drug-impaired prosecution. The hearings into the proposed changes to the Criminal Code of Canada highlights some of the legislative and policy challenges faced when attempting to address this issue specifically in law. A significant challenge identified has been to better document that a significant problem exists in addition to that which is already addressed by alcohol or impaired driving legislation. Any new legislation concerning drug impaired driving will happen within the context of the current environment of alcohol impaired driving enforcement. In many respects the situation is similar, but in others it is not. Legislators, the judicial system and the public are aware of the situation with alcohol impaired driving which may help or hinder new policies and programs related to drug impaired driving. It will be important to exploit the similarities but identify and manage the differences in order to bring in legislation which is both effective and accepted by the police, courts, and drivers. This includes making the requisite resources available to support any legislative or policy changes implemented. It will be very important to ensure that new legislation does not complicate prosecutions or existing sanctions for alcohol 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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.028 | 0.017 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.013 | 0.006 |
| Research integrity | 0.111 | 0.076 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".