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Record W4411788881 · doi:10.4000/148mr

Enjeux environnementaux : quand les « travaux terrains » s’arriment avec l’école secondaire pour le développement d’une écocitoyenneté chez des élèves de 12 à 14 ans en S&T au Québec

2025· article· fr· W4411788881 on OpenAlexaboutno aff
Catherine Simard, Ghislain Samson, Émilie Morin, Geneviève Therriault

Bibliographic record

VenueÉducation et socialisation · 2025
Typearticle
Languagefr
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsPhilosophyPolitical scienceArt

Abstract

fetched live from OpenAlex

Face aux défis environnementaux affectant des écosystèmes et la biodiversité, le programme pédagogique Opération PAJE propose une approche renouvelée de l’enseignement des sciences et technologies (S&T) au niveau secondaire et appliquée à la proximité d’écoles québécoises. Fondé sur l’apprentissage expérientiel à visée émancipatrice, ce programme intègre des travaux terrains aux contenus curriculaires, incitant les élèves à concevoir des solutions concrètes aux enjeux environnementaux identifiés localement. La présente étude examine ce dispositif pédagogique comme un levier permettant aux jeunes de s’engager activement face aux problématiques environnementales rencontrées dans leur communauté. Des groupes de discussion et des dessins, les résultats quant aux retombées éducatives chez l’élève indiquent que la réalisation de projets ancrés au territoire donne un sens aux apprentissages scolaires, tout en leur procurant un sentiment de fierté et de responsabilité vis-à-vis des effets positifs observés de leurs actions. Parmi les apprentissages observés figurent ceux d’ordre cognitif, affectif et comportemental, dont l’identification de la biodiversité, le fait de se sentir en interrelation avec la nature, d’avoir le sentiment d’aider la biodiversité par leurs actions et d’en prendre soin.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.385
Threshold uncertainty score0.775

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.003
Scholarly communication0.0060.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.043
GPT teacher head0.315
Teacher spread0.272 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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