Bibliographic record
Abstract
PHARMACEUTICAL POLICY IN CANADA IS SET AT both the federal and provincial levels of government. The federal government is responsible for intellectual property rights of manufacturers (patents) and the initial approval and labelling of prescription drugs and for ensuring overall market competitiveness. The provincial government has responsibility and jurisdiction over the funding of all health care services, in-cluding pharmaceuticals. Various interactions between the pharmaceutical indus-try, the federal and provincial governments and consumers have shaped the cur-rent landscape for prescription drugs in Canada. One key failing of the system is that the federal government is almost completely insulated from the impact of its policies because, although it regulates drug prices, it does not buy any drugs. In contrast, provincial governments have no jurisdiction over market competitiveness or pricing, yet end up paying for most of the drug expenditures incurred. Most Canadians will proudly admit that our publicly funded, universal ac-cess medicare system is worth a lot. Canadians seem willing to accepthigher taxes, lower wages and a weaker dollar as the price to pay for hav-ing everyone in insured — unlike the situation in the United States. Moreover,
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.189 | 0.044 |
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".