Bibliographic record
Abstract
The following questions facilitate further thought on the issue of reimportation: POLICY ISSUES: Who should pay for drug development? Do NCEs provide value for money invested? What is the most efficient means of developing new drugs? What is the proper balance between societal benefit and intellectual property protection? REGULATORY QUESTIONS: If reimportation or importation is permitted, how can the provenance of a product be protected? How is reimportation defined? Can a product be transported from the United States to Europe to Canada and then be sent back to the United States? Or, is reimportation a single-step process (e.g., United States to Canada and vice versa)? If reimportation is limited to Canada, can we expect that other countries would want to be included (or excluded) from our reimportation policy? Will state pharmacy practice acts be applicable to reimportation? Does reimportation alter the balance between state and federal regulations? If legal action occurs because of alleged harm from a reimported product, who is liable? MARKET ISSUES: Will U.S. prices rise in protected markets to compensate for losses due to reimports? Will Canadians be allowed to import prescription drugs from the United States?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".