Emerging illnesses and society : negotiating the public health agenda
Bibliographic record
Abstract
How do new diseases become part of the public health agenda? Emerging Illnesses and Society brings together historians, sociologists, epidemiologists, public health experts, and others to explore this vital issue. Contributors describe the processes by which patients' groups interact with medical researchers, public health institutions, and the media to identify and address previously unknown illnesses, including multiple sclerosis, Tourette syndrome, AIDS, lead poisoning, Lyme disease, and hepatitis C. The introductory chapter develops a general theoretical model of the social process of emergingillness, identifying critical epidemiologic, social and political factors that shape different trajectories toward the construction of public health priorities. Through case studies of individual diseases and analyses of public awareness campaigns and institutional responses, this timely volume provides important insights into the medical, social, and economic factors that determine why some illnesses receive more attention and funding than others. Contributors: Deborah Barrett, University of North Carolina, Chapel Hill; Steven Epstein, University of California, San Diego; Phyllis Freeman, University of Massachusetts, Boston; Diane E. Goldstein, Memorial University of Newfoundland; Peter J. Krause, University of Connecticut School of Medicine; Howard I. Kushner, Emory University; Lawrence D. Mass, Beth Israel Medical Center, New York; Michelle Murphy, University of Toronto; Lydia Ogden, Global AIDS Program, CDCR; Sandy Smith-Nonini, Elon University; Ellen Griffith Spears, Southern Regional Council; Andrew Spielman, Harvard School of Public Health; Colin Talley, University of California San Francisco; Sam R. Telford III, Harvard School of Public Health; Christian Warren, New York Academy of Medicine.
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 | 0.080 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.041 | 0.110 |
| Scholarly communication | 0.054 | 0.066 |
| Open science | 0.004 | 0.044 |
| Research integrity | 0.035 | 0.032 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".