Quick Response Disaster Research: Opportunities and Challenges for a New Funding Program
Bibliographic record
Abstract
Abstract Quick response research conducted by social scientists in the aftermath of a disaster can reveal important findings about hazards and their impacts on communities. Research to collect perishable data, or data that will change or be lost over time, immediately following disaster has been supported for decades by two programs in the United States, amassing a collection of quick response studies and an associated research culture. That culture is currently being challenged to better address power imbalances between researchers and disaster-affected participants. Until recently, Canada has not had a quick response grant program. In order to survey the state of knowledge and draw from it in helping to shape the new program in Canada, this article systematically analyzes the body of research created by the two US programs. The results reveal a wide-ranging literature: the studies are theoretically, conceptually, topically, and methodologically quite unique to one another. This diversity might appropriately reflect the nature of disasters, but the finding that many studies are not building on previous quick response research and other insights indicate opportunities for how a new grant program in Canada can contribute to growing a robust subdiscipline of disaster research.
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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 | 0.245 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.019 | 0.026 |
| Scholarly communication | 0.031 | 0.026 |
| Open science | 0.008 | 0.028 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".