Real‐time Responsiveness for Ethics Oversight During Disaster Research
Bibliographic record
Abstract
Disaster research has grown in scope and frequency. Research in the wake of disasters and during humanitarian crises--particularly in resource-poor settings--is likely to raise profound and unique ethical challenges for local communities, crisis responders, researchers, and research ethics committees (RECs). Given the ethical challenges, many have questioned how best to provide research ethics review and oversight. We contribute to the conversation concerning how best to ensure appropriate ethical oversight in disaster research and argue that ethical disaster research requires of researchers and RECs a particular sort of ongoing, critical engagement which may not be warranted in less exceptional research. We present two cases that typify the concerns disaster researchers and RECs may confront, and elaborate upon what this ongoing engagement might look like--how it might be conceptualized and utilized--using the concept of real-time responsiveness (RTR). The central aim of RTR, understood here as both an ethical ideal and practice, is to lessen the potential for research conducted in the wake of disasters to create, perpetuate, or exacerbate vulnerabilities and contribute to injustices suffered by disaster-affected populations. Well cultivated and deployed, we believe that RTR may enhance the moral capacities of researchers and REC members, and RECs as institutions where moral agency is nurtured and sustained.
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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 | 0.276 | 0.271 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.098 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.013 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 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".