“We never even touched plants this way”: school gardens as an embodied context for motivating environmental actions
Bibliographic record
Abstract
A critical objective of environmental education (EE) is sustained and active care for the natural world. Numerous studies point to the inadequacy of information-centered pedagogies in motivating such ecologically responsive action. The primacy of action in effectively addressing environmental issues calls for conceptions of EE that emphasize the conative and affective domains of the human mind. While recent research studies have recognized the significance of affective approaches in EE, the role of sensory and embodied engagements, which ground such affective encounters, are relatively less understood. Based on a year-long facilitation of a school terrace-garden in a metropolitan city, we outline how situated and embodied interactions can foster an understanding of diverse ecological practices, and thus a different way of being with the natural world. The study indicates that multi-modal sensorimotor experiences, and the possibility of sharing these with others, motivate children to expand their sphere of environmental activities beyond the site of learning. Specifically, students extended their care-based interactions with the garden to their communities and broader ecological issues. These findings indicate that integrative forms of situated and embodied interactions with the living world can provide the generative force to learn, act, care, and live in ways that encourage ecological flourishing.
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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 | 0.000 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".