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Record W4388783565 · doi:10.22230/ijepl.2023v19n2a1355

A Walking Curriculum: From “Good Ideas for Walks” to Transformative Design for Eco-Social Change

2023· article· en· W4388783565 on OpenAlexafffundvenue
Gillian Judson, Michael Datura

Bibliographic record

VenueInternational Journal of Education Policy and Leadership · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsSimon Fraser University
FundersSimon Fraser University
KeywordsTransformative learningCurriculumSociologyMeaning (existential)Resource (disambiguation)PedagogyPsychologyComputer science

Abstract

fetched live from OpenAlex

This pilot implementation study examines the experiences of ten teachers who have employed a place-based learning resource called A Walking Curriculum for one to three years. A Walking Curriculum is an example of Imaginative Ecological Education—a pedagogical approach that centralizes imaginative engagement, emotional connection, and somatic understanding in place-based learning. Initially, researchers sought to understand teachers’ practices and to determine how (or if) A Walking Curriculum provided teachers with a deeper insight into the principles of Imaginative Ecological Education underlying it. The research focus shifted to the nature of professional development and the meaning of educational change in a more-than-human world. This article considers policy implications of an ecological model of educational change that might better align with the eco-social transformation intentions of Imaginative Ecological Education.

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)0.015
metaresearch head score (Gemma)0.017
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 score0.015
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0040.005
Open science0.0020.008
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.224
GPT teacher head0.424
Teacher spread0.200 · 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

Citations4
Published2023
Admission routes3
Has abstractyes

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