Ethical implications of diversity in disaster research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Enhancing the effectiveness, efficiency, and fairness of interventions is an increasing source of concern in the field of disaster response. As a result, the expansion of the disaster relief evidence base has been identified as a pressing need. There has been a corresponding increase in discussions of ethical standards and procedures for disaster research. In general, these discussions have focused on elucidating how traditional research ethics concerns can be operationalized in disaster settings. Less attention has been given to the exploration of the ethical implications of heterogeneity within the field of disaster research. Hence, while current efforts to discuss the ethics of disaster research in low-resource settings are very encouraging, it is clear that further initiatives will be crucial to promote the ethical conduct of disaster research. In this article, we explore how the ethical review of disaster research conducted in low-resource settings should account for this diversity. More specifically, we consider how the nature of the project (what?), sociopolitical and physical environment of research sites (where?), temporal proximity to the disaster event (when?), objectives motivating the research (why?), and identity of the stakeholders involved in the research process (who?) all relate to the ethics of disaster research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it