Bibliographic record
Abstract
A detailed exploration of leadership problems that can develop during public health crises such as the anthrax attacks, SARS, and Mad Cow disease. An imminent threat to the public health, such as the swine flu outbreak, is no time for a muddled chain of command and contradictory decision making.Who's In Charge? Leadership during Epidemics, Bioterror Attacks, and Other Public Health Crisesexplores the crucial relationships between political leaders, public health officials, journalists, and others to see why leadership confusion develops. Who's In Charge?begins by looking at the overarching issues of leadership, public health administration, and the threats of bioterrorism. It then examines five recent emergencies—the 2001 anthrax attacks and 1993 cryptosporidium outbreak in the United States, the 2003 SARS outbreak in Toronto, the 2001 foot-and-mouth disease crisis, and the decade-long battle against Mad Cow Disease in the U.K. A perfect text for schools in public health, or as a reference for elected officials at every level of government, the book shows how each event developed step-by-step to pinpoint specific leadership issues. Engaging and absorbing, the work presents official reports, medical literature, first-person accounts from officials and journalists, and discussions of the role of law enforcement and the military during health care emergencies.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.126 | 0.093 |
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".