U.S. Senate Testimony on the Dept. of Commerce OECD Drug Pricing Report
Bibliographic record
Abstract
The US government has embarked on a trade strategy to address alleged free riding by raising patented prescription drug prices abroad. This strategy is unwise and dangerous. It is likely to 'succeed' in low and middle income countries, desperate to sign a bilateral trade agreement with the US. Increasing drug prices in these countries will greatly damage health, given relatively high demand elasticities for pharmaceuticals in these populations; modest cash resources for health care; and grave health needs. In short, the strategy will kill people in poorer countries. Most drug sales and profits are in the rich OECD countries, but the US trade strategy will be ineffective in these countries. The EU and Canada will likely resist any US attempt to interfere with a core domestic policy such as drug reimbursement. The recent experience in the US-Australia Free Trade Agreement bodes ill for the US strategy.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.014 | 0.008 |
| Insufficient payload (model declined to judge) | 0.052 | 0.028 |
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".