The More Things Change…: The Federal Government's Role In The Evaluative Sciences
Bibliographic record
Abstract
The unfortunate political history of the Agency for Health Care Policy and Research (AHCPR) illustrates the risks to the agencies attempting to evaluate the common practices of medicine and reform clinical decision making to take account of patients' preferences. The evaluative sciences have yet to regain the congressional attention they had when Senators George Mitchell and David Durenberger championed their cause. But the fundamental problems remain, and they are getting worse. Sooner or later Congress will need to revisit the debate over where in the federal government the evaluative sciences should find their base, and questions concerning the role of the National Institutes of Health (NIH) will be raised once again, as they were at the time of AHCPR's founding.
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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 | 0.027 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.020 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.121 | 0.087 |
| Insufficient payload (model declined to judge) | 0.006 | 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".