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Record W4408608729 · doi:10.31219/osf.io/jyqav_v1

Outdoor Pedagogy in Quebec Higher Education: A Current Portrait of the Situation

2025· preprint· en· W4408608729 on OpenAlexaboutno aff
François Bissonnette, Patrick Daigle, Yannick Lacoste, Joanie Beaumont, Laurence Couture-Wilhelmy, Holly McIntyre, Tegwen Gadais

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsnot available
Fundersnot available
KeywordsPortraitCurrent (fluid)PedagogyArtSociologyVisual artsPolitical scienceEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Background: The decline in physical activity and mental well-being among youth, has increased interest in and use of outdoor pedagogy (OP) as a potential solution. While the benefits of OP appear to be recognized in primary and secondary education, its use in Quebec’s (Canada) higher education institutions (HEI), including CEGEPs and universities, remains underexplored. Method: A mixed-method study was conducted to examine OP practices in Quebec HEI. An online survey was used to collect data on teachers’ profiles, pedagogical approaches, and motivations, among other factors. Results: Thirty-six teachers participated, representing various disciplines, though physical education and outdoor intervention were often overrepresented. College teachers favoured diverse and immersive environments, while university teachers tended to use peri-urban forests. Their motivations for adopting OP included raising environmental awareness, developing skills specific to outdoor contexts, and leveraging OP as a learning tool. Conclusion: This study provides an initial overview of OP practices in Quebec’s HEI, highlighting motivations, challenges, and more. Motivations for using OP converge on the importance of pedagogical authenticity and student benefits, such as stress reduction and improved well-being.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.397

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0100.003
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.000

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 head0.439
Teacher spread0.402 · 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 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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