Catastrophes are different from disasters: Some implications for crisis planning and managing drawn from
Bibliographic record
Abstract
Hurricane Katrina has reinforced the view of some researchers that the scale of any collective crisis has to be taken into account in any analysis.To them, just as “disasters ” are qualitatively different from everyday community emergencies, so are “catastrophes ” a qualitative jump over “disasters”.. Systematic social science study of disasters (natural and technological) is about a half-century-old. One of the first problems addressed by the pioneer researchers was in what ways disasters as social occasions differed from everyday emergencies. In less than a decade of field research it was conclusively documented that community disasters were qualitatively and quantitatively different from routine emergencies. At the organizational level alone there are at least four differences: (1) In disasters compared to everyday emergencies, organizations have to quickly relate to far more and unfamiliar converging entities. One study of what was a major but nonetheless community limited massive plant fire in Canada found that 348 organizations appeared on site. They included seven departments
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".