Between Market and State: Directions in Social Science Research on Disaster
Bibliographic record
Abstract
Governing after Crisis: The Politics of Investigation, Accountability, and Learning. Edited by Arjen Boin, Allan McConnell, and Paul 'T Hart. New York: Cambridge University Press, 2008. 336p. $99.00 cloth, $34.99 paper. Learning from Catastrophes: Strategies for Reaction and Response. Edited by Howard Kunreuther and Micheel Useem. Upper Saddle River, NJ: Wharton School Publishing, 2010. 352p. $37.99 paper. The Next Catastrophe: Reducing Our Vulnerabilities to Natural, Industrial, and Terrorist Disasters. By Charles Perrow. Princeton: Princeton University Press, 2007. 388p. $29.95 paper. Developed and developing nations alike face low-probability but high-consequence exogenous shocks, including ice storms, chemical spills, terrorist attacks, and regional blackouts. Recently, “natural” disasters have dominated the airwaves; mega-catastrophes that claim more than 1,000 lives have become an almost yearly occurrence. In 2010, the Haiti and Chile earthquakes killed more than 200,000 people between them and felt all too familiar to many observers in the West. Before them were Cyclone Nargis in Burma, which took 130,000 lives in 2008; Hurricane Katrina, which killed more than 1,500 New Orleans residents and left 80% of the city flooded in 2005; and the Indian Ocean tsunami, which claimed roughly a quarter of a million lives in India, Indonesia, Sri Lanka, and Thailand in 2004.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.004 | 0.043 |
| Scholarly communication | 0.015 | 0.041 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 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".