Haitian people's expectations regarding post‐disaster humanitarian aid teams’ actions
Bibliographic record
Abstract
The way people at the receiving end of humanitarian assistance perceive this intervention may provide invaluable bottom-up feedback to improve the quality of the intervention. We analyzed and mapped Haitians' views regarding international humanitarian aid in cases of natural disaster. Two hundred fifty participants-137 women and 113 men aged 18-67-who had suffered from the consequences of the earthquake in 2010 were presented with a series of vignettes depicting a humanitarian team's action and were asked to what extent these actions corresponded to what ought to be expected from an international aid mission. Four factors were considered in the vignettes (a) whether the team worked in close association with local institutions, (b) whether it was composed of competent people who were prepared for their mission, (c) whether they treated people as a function of immediate needs, and (d) whether they behaved in a respectful way. Through cluster analysis, five qualitatively different positions were found: (a) Complete hostility to any kind of humanitarian aid (6%); (b) Strong hostility to humanitarian aid in the case of disrespectful attitudes or behaviors (4%); (c) Technical competence is the only factor that matters (25%); (d) Both manifestation of respect for the population and technical competence matter (38%) and (e) Undetermined (27%). Most Haitians expect humanitarian teams to be technically competent, but even very competent aid is not considered adequate if provided in an arrogant and disrespectful manner.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".