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Record W4225620512 · doi:10.1080/13504622.2022.2061919

Outdoor education in Canadian public schools: Connecting children and youth to people, place, and environment

2022· article· en· W4225620512 on OpenAlexafffundabout
Morten Asfeldt, Rebecca J. Purc‐Stephenson, Thomas Zimmerman

Bibliographic record

VenueEnvironmental Education Research · 2022
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsOutdoor educationStewardship (theology)Experiential learningEnvironmental educationVariety (cybernetics)Environmental stewardshipPedagogySociologyConsciousness raisingExperiential educationPublic relationsPsychologyEnvironmental resource managementPolitical science

Abstract

fetched live from OpenAlex

The roots and goals of outdoor education (OE) in Canada are often linked to the Canadian summer camp tradition that emerged in the early 1900s which centered around character development, and the environmental movement of the 1950s and 1960s. However, a comprehensive understanding of the philosophies, goals, and activities of modern Canadian OE K-12 programs is unclear. Therefore, the purpose of this study is to identify the underlying philosophies, learning goals, and activities of K-12 OE programs in Canada. Using a descriptive research design, we conducted a web-based survey consisting of closed-and open-ended questions of 100 K-12 programs across Canada. Our findings indicate the programs are grounded in hands-on experiential learning that is holistic and integrates knowledge from a variety of disciplines. Primary learning goals include personal growth, community building, environmental stewardship, and people and place consciousness. OE activities varied but commonly included basic outdoor skills that can be practiced regardless of seasons. Implications for K-12 programming are discussed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.023
GPT teacher head0.352
Teacher spread0.330 · 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 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

Citations12
Published2022
Admission routes3
Has abstractyes

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