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Record W4410485176 · doi:10.1016/j.ssaho.2025.101580

But what about the ecological capabilities? Relationality and school-based food gardening in the australian early years learning framework

2025· article· en· W4410485176 on OpenAlexaff
Rachael Walshe, Ann Maxwell Hill, Bethaney Turner, Naomi Zouwer

Bibliographic record

VenueSocial Sciences & Humanities Open · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsEcologyGeographySociologyBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.002
Scholarly communication0.0040.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.086
GPT teacher head0.375
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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