A Traditional Closed-Loop Sanitation System in a Chronic Emergency: A Qualitative Study from Afghanistan
Bibliographic record
Abstract
The use of closed-loop sanitation systems (CLSS), or reuse-oriented sanitation systems, has increased in recent years, and such systems have been successfully implemented in many parts of the world. However, no research has explored Traditional CLSS (T-CLSS) for a long-term humanitarian situation. This study explores the strengths, weaknesses, opportunities and threats (SWOT) of T-CLSS in peri-urban and rural contexts in three different provinces in Afghanistan (the first study of its kind in Afghanistan). Participatory research tools, such as transect walks, focus group discussions, smart community gatherings and interactive workshops, were applied to assess the SWOT associated with T-CLSS. The results indicate that T-CLSS has been practiced historically in both peri-urban and rural areas using local and traditional knowledge, skills and technologies. The socio-cultural acceptance of the system in both rural and peri-urban areas is an important strength of this established system. However, due to chronic development challenges in the study regions, T-CLSS may possibly lead to exposure to microbial contaminants. It is recommended that the feasibility of an improved CLSS be assessed and implemented in light of the issues that are inherent in the use of T-CLSS in Afghanistan.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".