But what about the ecological capabilities? Relationality and school-based food gardening in the australian early years learning framework
Bibliographic record
Abstract
Reflecting on students' personal and social capabilities is essential for individual and societal benefits. Typically, the focus is on emotional awareness, collaboration, resilience, conflict resolution, and learning to function within school settings. However, there is little emphasis on building ecological capabilities. As we face climate uncertainty, understanding how to exist in a changing world is crucial, and developing ecological capabilities is now of utmost importance for our survival. This paper aims to demonstrate that ecological learning (and the accompanying capabilities) is inherently linked to personal-social development and suggests that education should move beyond human-centric approaches. We observed a 10-week gardening program at an Early Childhood School in ACT/Ngunnawal country. Our analysis reveals that students show empathy towards plants, engage confidently with human and non-human participants, understand food sharing, and make autonomous decisions. They also develop self-regulation skills and consider the well-being of non-human participants. Students extend their learning beyond the classroom, showing resilience in diverse multispecies contexts. By integrating ecological understanding with personal-social development, our findings demonstrate the benefits of non-human companionship and ecological connections in education, which are crucial for navigating ecological uncertainty.
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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 | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".