Pre-Implementation Perceptions Among Teachers on the Use of Ecological Sanitation and Application of Human Urine as Fertilizer
Bibliographic record
Abstract
Although human urine contains nutrients for plant growth, very few community schools in Nepal use a urine diversion dry toilet (UDDT) and apply the human urine as fertilizer in their school garden. Using human urine in agriculture reduces the use of chemical fertilizers, thus saving the expenditure associated with it. Application of human urine improves the soil fertility and may contribute to increased food security among school children if the school can supply the canteen with food for mid-day meals. This study adopted a Participatory Action Research (PAR) approach in order to understand stakeholder perspectives and involve them in the planning and implementation of urine diverting toilets. The data for this study were collected from five teachers’ focus group discussions. This paper presents teachers’ perceptions of the urine diversion dry toilet system and use of human urine as a fertilizer for the school garden. Only a few teachers accepted that human urine could be used as fertilizer, however, they were not willing to use it on their crops since it was considered impure. Due to a perceived bad odor and the uncomfortable sitting position on the UDDT, particularly for females, teachers disliked this toilet and they felt using urine as fertilizer was unnecessary. One of the key lessons drawn from the study is that schools, in collaboration with local governments, should employ participatory approaches to understanding and engaging local stakeholders, including teachers, to minimize negative perceptions prior to the application of human urine as fertilizer in the school garden.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".