In Search of the Golden Years: How Compulsory Licensing Can Lower the Price of Prescription Drugs for Millions of Senior Citizens in the United States
Bibliographic record
Abstract
This article will show that compulsory licensing is the best remedy for the escalating cost of prescription drugs in the United States. Section II will provide a historical overview of American pharmaceutical patent law and will introduce the concept of compulsory licensing as a method to decrease the high cost of prescription drugs for senior citizens in the United States. Section III will look at the newly enacted Medicare Prescription Drug and Modernization Act, and state and local government plans to import cheaper brand-name prescription drugs from Canada. Section IV will look at the United States' international support for compulsory licensing, as seen with the signing of the Agreement on Trade Related Aspects of Intellectual Property Rights. Next, this section will show that United States case law supports the implementation of compulsory licensing when a corporation has violated antitrust laws. Finally, this section will respond to arguments that have been made against compulsory licensing. Section V will propose the creation of a tripartite health care commission that will implement compulsory licensing in the United States and will sponsor legislation that responds to the health care crisis in the United States. Additionally, this section will propose that the multinational pharmaceutical companies license patents to, and enter into outsourcing agreements with, Indian pharmaceutical companies to reduce manufacturing costs, which will eventually balance the profit-making interests of pharmaceutical companies with the health care interests of the American public. Section VI will conclude this analysis and restate the idea that America's elderly deserve better treatment from their country and that compulsory licensing and an alliance with the Indian pharmaceutical industry are effective remedies for bringing down the high costs of prescription drugs in America.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".