‘They came with cholera when they were tired of killing us with bullets’: Community perceptions of the 2010 origin of Haiti’s cholera epidemic
Bibliographic record
Abstract
In 2010 following a catastrophic earthquake, Haiti saw the beginning of what would become the world's largest cholera epidemic. Nepalese United Nations peacekeepers were later implicated as the source of cholera. Our research examines Haitian community beliefs and perceptions, six-and-a-half years after the outbreak began, regarding the origin of Haiti's cholera outbreak. A narrative capture tool was used to record micronarratives of Haitian participants surrounding ten United Nations bases across Haiti. Seventy-seven micronarratives focused on cholera were selected for qualitative analysis from a larger dataset. Three themes emerged: who introduced cholera to Haiti, how cholera was introduced to Haiti, and preventative measures against cholera. With varying levels of confidence, the origins of the epidemic were conceptualised as directly resulting from the actions of the United Nations and Nepalese peacekeepers, exhibiting a distrust of foreign intervention in Haiti and frustration with inadequate water and sanitation infrastructure that facilitated widespread transmission of cholera. This study reinforces the need for additional transparent communication from the UN to address ongoing misconceptions surrounding the cholera outbreak, action to clean water and sanitation practices in Haiti, and for the voices of Haitian citizens to be heard and included in reforming foreign aid delivery in the country.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".