Ethical implications of diversity in disaster research
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.
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 | 0.493 | 0.457 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.118 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.014 | 0.023 |
| 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; 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".