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Record W3136786065 · doi:10.25071/1916-4467.40610

A Walking Curriculum: Learning From Risk and Connection

2021· article· en· W3136786065 on OpenAlexaffvenue
Astrid Steele

Bibliographic record

VenueJournal of the Canadian Association for Curriculum Studies · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsNipissing University
Fundersnot available
KeywordsCurriculumVariety (cybernetics)SurrenderNatural (archaeology)Space (punctuation)Connection (principal bundle)PsychologyMathematics educationPhysical educationSociologyPedagogyComputer scienceArtificial intelligenceHistoryPolitical scienceEngineeringLaw

Abstract

fetched live from OpenAlex

The act of walking has been described as “an exquisitely coordinated and elegant falling forward and catching oneself” (Kabat-Zinn, 2013, p. 125). Each step that we take is a physical risk in which we surrender our bodies into space, and only when our feet (re)connect with the earth do we find stability and are able to move forward. I propose that risk and connection are critical elements of a walking curriculum within an environmental education course for teacher candidates. The concept of risk is explored, and I describe a variety of course activities that involve taking physical, emotional or professional risks. The concept of connection is also examined with a particular focus on humans as integral to the natural world; and again, I describe course activities that provide opportunities for teacher candidates to experience connections to the natural world and to each other. Environment as the third teacher is explored, and lastly I reflect on my position as the instructor who facilitates learning opportunities for the teacher candidates in our course.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0050.005
Open science0.0010.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.002

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 head0.009
GPT teacher head0.243
Teacher spread0.235 · 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 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

Citations2
Published2021
Admission routes2
Has abstractyes

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Same venueJournal of the Canadian Association for Curriculum StudiesSame topicEnvironmental Education and SustainabilityFrench-language works237,207