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Record W3113548827

Exploring the national scope of outdoor nature-based early learning programs in Canada: Findings from a large-scale survey study

2020· article· en· W3113548827 on OpenAlexaffabout
Debra Harwood, Elizabeth Y. S. Boileau, Ziad F. Dabaja, Karen Julien

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsUniversity of WindsorLakehead University
Fundersnot available
KeywordsPopularityCurriculumOutdoor educationScope (computer science)Scale (ratio)Early childhoodEarly childhood educationPsychologyMedical educationPedagogyGeographyMedicineCartographyDevelopmental psychologyComputer scienceSocial psychology
DOInot available

Abstract

fetched live from OpenAlex

Across Canada, early learning nature-based programs are gaining popularity with many new programs being implemented each year. Currently, little is known about the number, type, pedagogies, and curricula content of Canadian outdoor and nature-based early learning programs. Thus, this mixed methods study was conducted to explore this growing movement. In total, two hundred educators, representing 165 various programs across Canada completed an online survey. Fifty-one percent of the participants reported having a diploma in Early Childhood Education or similar qualification. In addition, it was estimated that between 40 000 to 60 000 Canadian children, mostly aged between 3 to 9 years, had taken part in these programs during 2018-2019. Moreover, findings suggested that weather conditions can impact the time spent outdoors and that emergent, child-centered curricula rooted in play were guiding the pedagogy of a large percentage of the represented programs.

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 score0.010
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.152
GPT teacher head0.333
Teacher spread0.181 · 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

Citations20
Published2020
Admission routes2
Has abstractyes

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