Bibliographic record
Abstract
Winner of the 2018 Arthur J. Viseltear Award from the Medical Care Section of the American Public Health Association Children and Drug Safety traces the development, use, and marketing of drugs for children in the twentieth century, a history that sits at the interface of the state, business, health care providers, parents, and children. This book illuminates the historical dimension of a clinical and policy issue with great contemporary significance—many of the drugs administered to children today have never been tested for safety and efficacy in the pediatric population. Each chapter of Children and Drug Safety engages with major turning points in pediatric drug development; themes of children’s risk, rights, protection and the evolving context of childhood; child-rearing; and family life in ways freighted with nuances of race, class, and gender. Cynthia A. Connolly charts the numerous attempts by Congress, the Food and Drug Administration, the American Academy of Pediatrics, and leading pediatric pharmacologists, scientists, clinicians, and parents to address a situation that all found untenable. Open access edition funded by the National Endowment for the Humanities. The text of this book is licensed under a Creative Commons Attribution NonCommercial-NoDerivatives 4.0 International License: https://creativecommons.org/licenses/by-nc-nd/4.0/
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.054 | 0.018 |
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".