Small Change is Beautiful: Exploring Possibilities of Eco-san on School Garden for Transformative Pedagogy
Bibliographic record
Abstract
This article explores the possibilities of the use of eco-san in the school garden in the Nepalese community school, focusing on transformative pedagogical impacts on the social learning environment of the school. In particular, the use of urine as a fertilizer in the school garden through eco-san and linking pedagogical alignment to provide a pleasant experience that has a positive impact on students’ meaningful engagement, social connections, and developing confidence. The main finding for the research question came from qualitative data collected from students, parents, and teachers through in-depth interviews, focus group discussions, participant observations and informal conversations. This was supported by analyses of qualitative data on students’ learning, collaborative inquiry, teachers' and parents' engagement and perceptions on the use of human urine as fertilizer from eco-san, and school gardening activities. Results showed that the gardening program attributes valued most highly by the parents and teachers included increased students' meaningful engagement, opportunities for experiential and integrated learning through dialogue conferences, collaborative inquiry, and building social skills like cooperation, sharing and argumentation. Future research should explore whether effects persist over time and if and how changes in students’ positive attitude affect learning through school gardening activities applying human. Suggestions for applying results to future studies are provided.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".