Ending Drug Registration Apartheid: Taming Data Exclusivity and Patent/Registration Linkage
Bibliographic record
Abstract
The pharmaceutical industry's dependence on intellectual property rights (IPRs), especially patents, to exclude competitors and thereby recoup past expenditures, incentivize future investments in research and development (R&D), and maximize profits is well known. Although initially content to solidify patent rights in the rich-country markets of North America, Europe, and Japan, in the last quarter of the 20th century the industry has increasingly turned its attention to emerging markets in Latin America, Asia, and even Africa as sites of future market expansion. Big middle-income countries like Brazil, India, China, and Indonesia have growing middle classes that increasingly favor allopathic medicine over the more traditional medicines of their elders. Obtaining monopoly rights in these growing markets could help the pharmaceutical industry weather the storm of increased consumer, business, and government blow-back against supra-competitive drug prices charged in rich country markets.
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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 | 0.059 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.011 | 0.027 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".