Conclusion: How to Right the Trade Imbalance
Bibliographic record
Abstract
On October 21, 2003, a group of senior citizens from the U.S. state of Minnesota traveled to Canada, where they planned to commit a crime – to purchase prescription drugs. In the United States, it is illegal to reimport drugs from abroad. But the price of prescription drugs in the United States has risen dramatically in recent years. So, instead of buying their medications at home, they bought their medicines at a licensed pharmacy in Winnipeg, Canada, where prescription drug prices are significantly lower. The next day, the seniors took the bus home. As the bus steered back onto U.S. territory, U.S. FDA agents boarded the bus and confronted the wily seniors. Although the agents didn't confiscate the medicines, they used their position of authority to dissuade the seniors from making future trips. The United States does not regulate the price of drugs or subsidize drug costs for many Americans. Drug prices are high and rising. Thus, many Americans buy their drugs on the Internet or, like the crafty seniors, buy drugs abroad. They travel to countries where governments purchase drugs for their citizens or regulate the price of medicines. But U.S. pharmaceutical manufacturers argue that when governments, such as Canada, use their monopsony power to drive down the cost of drugs, these governments distort the market for drugs. These pharmaceutical manufacturers claim that the United States is the only country where they can charge market rates for the drugs they research, develop, and test.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.049 | 0.014 |
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".