Bioterrorism or Avian Influenza: California, The Model State Emergency Health Powers Act, and Protecting Civil Liberties During a Public Health Emergency
Bibliographic record
Abstract
INFLUENZA the globe.'°SARS spread from rural China to the hospitals of metropolitan Toronto, Canada, in four months and ultimately resulted in over 8,000 cases and nearly 800 deaths in twenty-seven countries."While not the pandemic many government and medical officials feared, the SARS experience represented a global threat due to its virility, 12 speed of dispersion, 3 and impact on medical care around the world.1 4 Consequently, both the fear of bioterror attacks and the realization that newly emerging viruses can disrupt the functioning of government, moved officials to address the issue of governing during and immediately after either type of event.Moreover, in the wake of Hurricane Katrina, "one of the worst natural disasters in our nation's history," and the threat posed by disease outbreak, the need for improved disaster relief preparedness remains evident.15 Under the direction of Mike Leavitt, Secretary of the Department of Health and Human Services, the federal government declared a public health emergency in the affected region.' 6 With floodwaters contaminated by sewage and decaying dead bodies, the threat of mosquito-borne disease, 17 as well as cholera, dysentery, and other infections, posed a fundamental risk to security and the cleanup process.'Although the federal government eventually appeared to 10. Robert A. Weinstein, Planning for Epidemics-The Lessons of SARS, 350 NEW ENG.J. MED.2332 (2004). Id.12. The overall death rate for SARS was approximately nine percent globally.Arshagouni, supra note 9, at 201.Young adults had an average death rate of approximately three percent and the average death rate in the elderly was about fifty percent.Id.13. Obijiofor Aginam, Between Isolationism and Mutual Vulnerability: A South-North Perspective on Global Governance of Epidemics in an Age of Globalization, 77 TEMP.L. REV.297, 307 (2004).The term "Jet-Spread" has been used to describe the swift travel of an infectious disease virus by airplane.RICHARD E. NEUSTADT & HARVEY V.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".