Cholera in the Time of MINUSTAH: Experiences of Community Members Affected by Cholera in Haiti
Bibliographic record
Abstract
In 2010, Haiti experienced one of the deadliest cholera outbreaks of the 21st century. United Nations (UN) peacekeepers are widely believed to have introduced cholera, and the UN has formally apologized to Haitians and accepted responsibility. The current analysis examines how Haitian community members experienced the epidemic and documents their attitudes around accountability. Using SenseMaker, Haitian research assistants collected micronarratives surrounding 10 UN bases in Haiti. Seventy-seven cholera-focused micronarratives were selected for a qualitative thematic analysis. The five following major themes were identified: (1) Cholera cases and deaths; (2) Accessing care and services; (3) Protests and riots against the UN; (4) Compensation; and (5) Anti-colonialism. Findings highlight fear, frustration, anger, and the devastating impact that cholera had on families and communities, which was sometimes compounded by an inability to access life-saving medical care. Most participants believed that the UN should compensate cholera victims through direct financial assistance but there was significant misinformation about the UN's response. In conclusion, Haiti's cholera victims and their families deserve transparent communication and appropriate remedies from the UN. To rebuild trust in the UN and foreign aid, adequate remedies must be provided in consultation with victims.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".