Sampling an unknown universe: problems of researching mass casualty incidents (a history of ECRU's field research)
Bibliographic record
Abstract
This paper reviews how the Emergency Communications Research Unit (ECRU) at Carleton University in Ottawa, Canada, developed its field research techniques with emphasis on some of its approaches to sampling. Then based on ECRU's experience, it discusses the problems that would arise if an attempt were made to research an incident involving not only mass casualties, but also chemically contaminated mass casualties. While ECRU's findings have been published in scores of book chapters, monographs and academic and other articles, this is only the second time since 1977 that its methods have been described [The Development of a Standby Research Capacity at Carleton University. Emergency Planning: Ottawa, Canada; Int. J. Mass Emergencies and Disasters 1977; 2(1):35-41; Methods of Disaster Research. Xlibris Corporation, 26-302], and the very first time its approaches to sampling have been discussed.
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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 | 0.243 | 0.323 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.010 | 0.076 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".