MétaCan
Menu
Back to cohort
Record W4410479221 · doi:10.1017/aee.2025.31

Wild Pedagogies and Young Children through the Mosaic Approach

2025· article· en· W4410479221 on OpenAlexafffund
A. Elizabeth Beattie, Sandra Scott, Douglas Adler

Bibliographic record

VenueAustralian Journal of Environmental Education · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMosaicSociologyPedagogyArtVisual arts

Abstract

fetched live from OpenAlex

Abstract This paper reports on a doctoral study that explored young children’s (ages 5 to 7 years) relationships with sticks during their school-based outdoor learning experiences. Sticks (parts of trees) became uniquely contextual agents due to the profound agentic effect the stick-based experiences, which were enacted through Wild Pedagogies, had on the children’s understandings of Place. Sticks were used in physical and symbolic ways throughout the children’s self-guided learning experiences. The children used long sticks to build large structures, houses, and other creations, and selected smaller sticks to represent microphones, brooms, or currency. The use of the Mosaic approach in this study aligns with Wild Pedagogies’ openness to new and different ways of being in and understanding the world, particularly as this approach privileges children, natural objects, and Place as agentic co-teachers and co-learners. The children demonstrated their agency as they made cognitive, physical, corporeal, agentic, affective, and aesthetic connections with Place, which they expressed through their Wild Pedagogical experiences. The study underscores the value of tactile, immersive, and bioregional experiences in helping children connect with nature, build knowledge, develop and share collective agency, and cultivate an ethic of care for the environment in Wild Pedagogical ways.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.635

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 head0.024
GPT teacher head0.268
Teacher spread0.243 · 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 designNot applicable
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

Citations1
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueAustralian Journal of Environmental EducationSame topicArt Education and DevelopmentFrench-language works237,207