Bibliographic record
Abstract
[1] Perhaps nothing permeates modern American society as much as prescription drugs. Evidence of this exists not just in television and magazine ads extolling the promises of Viagra and Nexium, but also in a few statistics. First, forty-six percent of Americans use at least one prescription drug daily. Further, in 2001, 3.1 billion prescriptions were issued in the United States at a cost of $132 billion. That amount is projected to increase to $414 billion by 2014. Such numbers explain the intensity of the recent political and legal debates surrounding prescription drugs, such as the importation of American pharmaceuticals from Canada and the issuance of prescriptions online without visiting a physician. During the 2004 presidential campaign, President Bush touted his Medicare Modernization Act, a significant component of which concerned
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".