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Record W7051803192

Outdoor Learning in Ontario: Toward an Ecological and Relational Education in Kindergarten

2025· article· en· W7051803192 on OpenAlexaboutno aff

Bibliographic record

VenueJournals & Books Hosting (International Knowledge Sharing Platform) · 2025
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsOutdoor educationHonorIndigenousEnvironmental educationCurriculumIndigenous educationPlace-based educationEcological psychologyConsciousness raisingTraditional knowledge
DOInot available

Abstract

fetched live from OpenAlex

The works of Donald (2020; 2021) emphasize the need to renew our relationships with places and to consider the Earth not merely as a physical space but as a living entity with which humans interact. This relationality redefines outdoor learning by positioning the land as an active co-teacher. The preparatory cycle and the PMJE program (Ministry of Education, 2016), with its focus on play-based learning, inquiry, and exploration, align closely with this educational vision. In such contexts, nature becomes not only a setting but an active participant in the learning process, reflecting a holistic and environmentally respectful approach (Chard, 2017; Brougère, 2005). The need to redesign pedagogical practices in Canada through ecological and cultural lenses has been highlighted by multiple scholars. Studies such as those by Chambers (1999; 2006) demonstrate that traditional curricula have long been shaped by Eurocentric or North American paradigms, often overlooking Indigenous relationships with the land. The preparatory cycle, as the initial stage of public schooling, presents a critical opportunity to introduce educational practices that honor these relationships and foster lasting environmental consciousness in young children. Keywords: outdoor learning, kindergarten, relationality, place-based connection, ecological education, early childhood teachers, reconciliation, ecological awareness. DOI: 10.7176/JEP/16-4-02 Publication date: April 30th 2025

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

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

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 head0.038
GPT teacher head0.300
Teacher spread0.262 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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