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Record W4401111572 · doi:10.1080/13664530.2024.2383748

Nature-based teacher education as beyond ‘getting outside:’ relational attunement, attending to the un-noticed, and ethical responsibility

2024· article· en· W4401111572 on OpenAlexafffund
Cher Hill, Paula Rosehart, Daniella Roze des Ordons, Kate Aileen, Sean Blenkinsop

Bibliographic record

VenueTeacher Development · 2024
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAttunementPedagogyPsychologyEthical responsibilityLegal responsibilityTeacher educationSociologySocial psychologyMathematics educationPublic relationsPolitical scienceMedicineLaw

Abstract

fetched live from OpenAlex

As in-service teacher educators, we take concerns about the environmental emergency and the crisis of colonialization seriously. We offer graduate programs in Place and Nature-based Experiential Learning to try and foster cultural change within mainstream schooling, while also preparing teachers for the immense challenge of teaching within the Anthropocene. In this article, we share four vignettes that describe experiences that contributed to important shifts in worldviews and ways of being with land for ourselves and/or our students, and we explore how this has led us to think differently about teaching and learning. Guided by Indigenous and eco-critical scholarship, we identified ecological pedagogical practices across the vignettes that contributed to such shifts, including relational attunement and attending to the un-noticed. We consider how this attending and attuning makes ethical demands that potentially puts us at odds with conventional dominant educational practices and expectations.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.012
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0120.022
Scholarly communication0.0050.004
Open science0.0010.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.376
Teacher spread0.350 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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