Policy Perspectives on Green School Guidelines: Connecting School Science with Gardens to Envision a Sustainable Future
Bibliographic record
Abstract
The purpose of this paper is to explore the perspectives of teacher educators and policy experts on ‘Green School Guidelines’ and ‘One Garden One School’ educational policies in Nepal. This paper also examines how these educational policies help to attain sustainable development goals through education for sustainable development. It aims to explore ways for effective implementation of these policies for activity-based science learning in the school garden. The qualitative method was used to explore the perspectives of science and environment teacher educators and central level policy experts. The data were collected from semi-structured in-depth interviews and informal conversations. The data from both these sources were analyzed thematically around the concepts of education for sustainable development, its implementation strategies and challenges, and life skills development among students through school gardening activities. The study found that teacher educators and policy experts positively view the Green School Guidelines and One Garden One School implementation strategies. Nevertheless, to achieve policy aims, local organization needs to play a major role in the effective implementation of green school guidelines. The findings from this study are expected to encourage the Nepal government, local governments, and community schools to bring central level policies into local practices.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.011 | 0.013 |
| 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".