Handling Mass Death by Integrating the Management of Disasters and Pandemics: Lessons from the Indian Ocean Tsunami, the Spanish Flu and Other Incidents
Bibliographic record
Abstract
At first glance, there appear to be significant differences between mass death from disasters and catastrophes and mass death from pandemics. In a disaster or catastrophe the major problem is identifying the dead and, sometimes, determining cause of death. This can be very frustrating for next of kin. In a pandemic, the identity of the dead is usually known as is the cause of their death. There is an immediate certainty in pandemic death. Despite these major differences there are many similarities. Because it takes time to identify the dead after a disaster or catastrophe, there is a steady release of bodies for cremation or burial, just as in a pandemic. In both types of incidents, there tends to be a shortage of supplies and personnel and, therefore, a need for use of volunteers. There are also massive amounts of paper work. This would suggest a need in both cases for stockpiling and for training of volunteers. And, although this does not always happen, both types of incidents tend to strike harder among the poorer elements in cities yet both create serious economic problems. Despite these many similarities, planning for the first tends to be done by emergency agencies, especially the police; planning for the second by health agencies. Given the many similarities this separation makes no sense. Since both types of mass death incidents lead to similar problems, it would make sense to take an all‐hazards approach to planning for dealing with mass death.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".