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Record W3037779425 · doi:10.1111/jade.12308

Conceptualising Art Education as Environmental Activism in Preservice Teacher Education

2020· article· en· W3037779425 on OpenAlexaboutno aff
Hilary Inwood, Alysse Kennedy

Bibliographic record

VenueInternational Journal of Art & Design Education · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental educationSustainabilityVisual arts educationPedagogyThe artsEnvironmental artQualitative researchSociologyCommunity engagementTeacher educationHigher educationPolitical sciencePublic relationsContemporary artSocial scienceArtEcology

Abstract

fetched live from OpenAlex

Abstract This article explores how art and design education can contribute to the imperative of climate change and help societies adapt to living more sustainably. Drawing on methods from arts‐based research and qualitative case study, it reports on an investigation into what can be learned from creating environmental art installations with preservice teachers (those training to be K‐12 teachers), as part of an environmental art education programme in a leading Canadian university. Findings support that preservice teachers experienced behavioural and attitudinal shifts towards sustainability after engaging in the processes of creating environmental art; involvement in the programme also provided opportunities for building community, engaging multiple domains of learning, modelling sustainable art‐making practices and prompting environmental activism. The results of this study inform a developing pedagogy for environmental art education in higher education settings.

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)0.004
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0080.041
Scholarly communication0.0130.006
Open science0.0020.008
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.037
GPT teacher head0.289
Teacher spread0.253 · 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 designTheoretical or conceptual
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

Citations18
Published2020
Admission routes1
Has abstractyes

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