Disaster-affected Populations and “Localization”: What Role for Anthropology Following the World Humanitarian Summit?
Bibliographic record
Abstract
The international humanitarian sector has long been criticized for relying on standardized responses that make little, if any, adjustment to social and cultural differences between different disaster contexts and disaster-affected populations. Responding to such criticisms, the 2016 World Humanitarian Summit set an ambitious target for “localizing” international humanitarian funding flows so that a quarter would be provided by local and national responders. But what precisely “local” might mean was little theorized, and what humanitarian agencies themselves could learn to improve their own aid-effectiveness from the disaster-affected populations’ own responses to severe stress was not prioritized. This article identifies some of the challenges the new funding regime needs to address for it to have the best chances of meeting its stated objectives, and it explores what role anthropology could play in researching such issues in an action-investigation frame. It concludes with some reflections about effective public anthropology in that conducive frame.
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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 | 0.034 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.064 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".