An assessment of Canada’s revised Patented Medicines (Notice of Compliance) Regulations
Bibliographic record
Abstract
Canada has high rates of generic usage, but the mechanisms of ensuring timely generic entry are dysfunctional. Unlike most other countries with a linkage regime in which regulatory approval depends on clearing patent hurdles, Canada does not offer temporary generic exclusivity. Instead, it grants the generic kept out of the market by a patent that is ultimately found invalid or not infringed the opportunity to seek its lost profits from the patentee. The system is unbalanced in that patentees find it profitable to litigate even very weak patents since they sustain a monopoly; while the incentives for generic firms to litigate, even if they expect to win, are very weak. This paper analyzes the incentives of the parties, and proposes possible policy fixes to help rebalance the system.
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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 | 0.030 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.009 | 0.005 |
| 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".