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Record W2567495605 · doi:10.18357/jcs.v41i1.15461

From Excuses to Encouragements: Confronting and Overcoming the Barriers to Early Childhood Outdoor Learning in Canadian Schools

2016· article· en· W2567495605 on OpenAlexvenueaboutno aff
Heather Coe

Bibliographic record

VenueJournal of Childhood Studies · 2016
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsnot available
Fundersnot available
KeywordsOutdoor educationPedagogyEarly childhoodIdeal (ethics)SociologyOutdoor activityPsychologyMathematics educationDevelopmental psychologyPolitical science

Abstract

fetched live from OpenAlex

<p>Drawing on outdoor education literature, this paper aims to address issues related to outdoor learning, and to confront some of the potential barriers and concerns that educators, administrators, parents, and researchers may have with regards to outdoor learning. While forest and nature-based programs provide an ideal educational setting for children to connect and interact with the natural world, they are not always easily accessible or practical for a majority of young Canadians. There are, however, approaches and ideas that can be drawn from these specialized outdoor early years programs and applied more broadly in contemporary urban and rural Canadian schools. A conceptual shift from a culture of excuses to a model of encouragement is presented, suggesting that educators should view outdoor learning as a pedagogical and problem-solving exercise.</p>

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.398

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0260.012
Scholarly communication0.0060.002
Open science0.0030.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.317
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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations12
Published2016
Admission routes2
Has abstractyes

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