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Record W4414989661 · doi:10.22215/cujs.v5i3.5490

Beyond The Classroom: Exploring Outdoor Learning in Canadian Schools

2025· article· en· W4414989661 on OpenAlexaffabout
Emma Simpson, Laurel Donison, Megan Zeni, Louise de Lannoy, Rachel T. Buxton, Kimberly Matheson, Tanya Halsall

Bibliographic record

VenueCarleton undergraduate journal of science. · 2025
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsCarleton University
Fundersnot available
KeywordsOutdoor educationWork (physics)Focus groupDiversity (politics)Experiential learningParticipant observation

Abstract

fetched live from OpenAlex

As a result of COVID-19, faults in the Canadian education system have been highlighted. Students are struggling to focus, learn and succeed. Alternative methods to learning and education need to be explored, outdoor learning (OL) being an important possible method in improving youth education. Research has shown that children have better health and academic outcomes when outdoor, nature-based learning is integrated into their schooling. The aim of the current study was to evaluate perspectives on OL experiences from youth, educators and policymakers to create a comprehensive understanding of the benefits, challenges and supports needed to enhance OL, with a focus on equitable access. This research project consisted of three parts: interviews with children, interviews with educators and a geographical scan with policymakers. We employed a framework that encouraged participant engagement so that the project was informed by interest holders and those impacted by the research. The interviews revealed six major themes, being risks; the role of administration; policy; equity; and a diversity of challenges. The results provided insight into the interconnection between policy-, administrator- and educator- level supports and buy-in necessary to enhance the benefits of OL so that they are felt by children within the classroom. This work contributes to a growing body of literature that describes holistic approaches to enhance education and wellness in children.

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)0.002
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 score0.239
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.028
GPT teacher head0.331
Teacher spread0.303 · 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 routes2
Has abstractyes

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