Price Discrimination in the United States: Why Are Pharmaceuticals Cheaper in Canada and Are Americans Seizing the Opportunities across the Border
Bibliographic record
Abstract
A group of elderly citizens climbs onto a bus that was sent by their U.S. senator.At first glance, it appears to be another political candidate's effort to ensure that these senior citizens make it to the polling booths.But as the bus crosses the U.S.-Canadian border, one realizes that these riders aren't headed to their precinct polling locations.Instead, they are traveling to Canada to purchase prescription drugs!When politicians focus campaign attention on the high cost of prescription drugs, they tend to aim their message at senior citizens because that group constitutes forty two percent of the prescription drug market.'Vermont Senator Bernard Sanders, inspired some of his political colleagues to organize bus trips to take senior citizens to Canada to purchase prescription medicines, in an effort to politicize, once again, the issue of rising prescription drug costs.2 Recently, Senator Debbie Stabenow of Michigan organized such a trip.Hundreds of senior citizens from across the nation climbed aboard to ride the Rx Express from Detroit to Windsor, Canada to purchase medicines.3 Senator Tim Johnson of South Dakota also scheduled a bus trip for senior citizens in August of 2002 to visit * Juris Doctorate candidate at SMU, graduating in December 2003.Bachelor of Science in Biology from
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".