Bibliographic record
Abstract
The problem of obtaining fair pricing for generic drugs has led to a series of regulatory measures in Canadian provinces. This paper offers a new way of thinking about the problems that need to be addressed, by considering three core components of the value chain of getting generic drugs to Canadians: litigation, production, and pharmacy services. The paper proposes that each component of this value chain should be paid for separately, using a royalty to reward successful litigation that benefits payers; a competitive market framework to pay for production; and a transparent, independent regulatory process to set dispensing fees for pharmacies. This approach would enable the total expenditures to match costs, would enable provinces to set appropriate quality and convenience standards for pharmacy, and would provide a measure of predictability for investors. The paper emphasizes that it is important to establish a separate mechanism for rewarding litigation that eliminates invalid patents. The savings to Canadians from such litigation exceeds one billion dollars annually. Without addressing the need to reward this valuable activity, it is dangerous for payers to drive down generic prices, since generic firms will lack incentives to invest in costly litigation. The paper also encourages governments to establish independent regulatory authorities to set fair fees for pharmacies by employing processes similar to those used in other price regulation agencies.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".