Exploring waste and sanitation-borne hazards in Rohingya refugee camps in Bangladesh
Bibliographic record
Abstract
Abstract Improper sanitation and waste management is the number one cause for ill health, disease and death throughout the world, particularly under extremely dense living conditions in refugee camps in the global South. This paper discusses the results of a mixed-method study conducted in Rohingya refugee camps, located in Chittagong, Bangladesh, currently hosting the world's largest concentration of refugees. Our structured questionnaire, group discussion and interviews were centered on waste-borne hazards. The research has evidenced severe challenges associated with overall precarious sanitation and waste situations in the camps. Garbage littering and open defecation are widely practiced. Congested drainage systems contribute to flooding, bringing waste and contaminants into people's homes. Improvements can be made by involving camp inhabitants in decision-making processes and giving them greater ownership in everyday infrastructure maintenance. Our research suggests that community participation is the key tool to maintain proper cleanliness of drains and toilets. Creating a stronger sense of community in the camps and practicing transparency and inclusion in planning and decision-making can contribute to addressing the key threats identified in this research and also apply to other refugee camps worldwide, with similar hazardous living conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".