1 Prescription Drug Importation, Investment and Employment in Michigan
Bibliographic record
Abstract
Authorization of the importation of prescription drugs from countries with lower prices than the United States is an increasingly attractive proposition for state and federal policymakers. The short-run benefit of reductions in prices of selected prescription drugs for selected patients may be viewed as a sufficient reason to favor importation. However, there may be longer-run effects of importation of prescription drugs that are less desirable, including reduced investments and altered regional investments in pharmaceutical research and development. With separate approval processes used in different markets, pharmaceutical companies practice price discrimination. Prices of prescription drugs are higher in the United States than in most countries that have negotiated discounts or set prices. Differences in prices between the United States and other countries of one-third to one-half may provide the opportunity for firms in a low-price country to export a drug to high-price country at a profit – giving rise to the concept of “importation ” from the perspective of the high-price country. Currently, importation of prescription drugs from Canada to the United States may exceed $1 billion. Public policies, such as current Senate Bill 2328, The Pharmaceutical Market Access and Drug Safety Act, could permit sales of imported prescription drugs. Prior research has
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".