Bibliographic record
Abstract
In June of this year, Health Canada put forth its proposal torender high-risk drugs tamper-resistant.1 This would entailchemical modifications designed to increase resistance tocrushing, chewing, cutting, and dissolution of the tablets.The primary targets of this policy were prescription opioidmedications used to manage acute and chronic pain. Recentstudies have demonstrated an alarming prevalence of themisuse of these drugs. In Ontario alone, opioid medicationabuse is responsible for the death of 1 in 170 people each year.2An additional 1 in 8 deaths in individuals aged 25-34 can beattributed to the same cause.2 Although some acknowledgethis tentative step taken by the federal government to addressthe rapidly escalating drug crisis as promising, many feel thatthis action is insufficient. Moreover, while more can be done toaddress the issue at hand, it is essential to avoid the stigmatizationof administering narcotics. Otherwise, patients who truly needthese drugs would be deprived of a satisfactory quality of life.
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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 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".