Third Circuit Accepts Preemption Arguments; More Cautions About Follow-On Biologics; Health Canada Issues Approval Guidance; FDA Takes First Steps in Expanding Drug Safety; Congress Proposes Requiring “Country of Origin” Statement; Congress Wants Direct Treatment Comparisons; Further Congressional Questions About Drug Advertising; Promised Studies Not Done; Warning over Avandia; Inspections of Foreign Facilities Will Not Be Easy; FDA Guidance on Genomics Term Definitions; European Scientists Want More Nanotechnology Regulation; Geron Stem Cell Trial on Hold; South Korea Clarifies Cloning Regulations; Decisions Needed on Antivirals in Advance of Flu Epidemic; Efforts to Control Genetic Testing; Bush Signs Genetic Information Nondiscrimination Act; Drug Companies Petition for Looser Restrictions on Literature on Off-Label Uses; Merck Said to Have Violated Publishing Ethics; Trade in Biotechnology Requires Closer Control; U.N. Alleges Chinese Dissidents Being Used as Organ Sources; Recent Approvals; Approval Delays?; NIH's Open Access Policy Still Has Publishers Roiled; Tilting at the FDA
Bibliographic record
Abstract
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.058 | 0.032 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".