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Record W2150048944 · doi:10.1177/097340820900400111

Runaway Climate Change as Challenge to the ‘Closing Circle’ of Education for Sustainable Development

2010· article· en· W2150048944 on OpenAlexaff
David Selby, Fumiyo Kagawa

Bibliographic record

VenueJournal of Education for Sustainable Development · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsMainstreamTransformative learningSustainable developmentVirtuous circle and vicious circleEducation for sustainable developmentClimate changeSociologyEnvironmental ethicsPolitical scienceEconomic growthPolitical economyEconomicsLawPedagogy

Abstract

fetched live from OpenAlex

Education for sustainable development (ESD) is the latest and thickest manifestation of the ‘closing circle’ of policy-driven environmental education. Characterised by definitional haziness, a tendency to blur rather than lay bare inconsistencies and incompatibilities, and a cozy but ill-considered association with the globalisation agenda, the field has allowed the neoliberal marketplace worldview into the circle so that mainstream education for sustainable development tacitly embraces economic growth and an instrumentalist and managerial view of nature that goes hand in glove with an emphasis on the technical and the tangible rather than the axiological and intangible. Runaway climate change is imminent but there is widespread climate change denial, including within mainstream ESD. A transformative educational agenda in response to climate change is offered here. Recent calls for the integration of climate change education (CCE) within mainstream education for sustainable development should be resisted unless the field breaks free of the ‘closing circle’.

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)0.006
metaresearch head score (Gemma)0.011
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: none
Teacher disagreement score0.011
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.023
Scholarly communication0.0110.012
Open science0.0010.009
Research integrity0.0090.013
Insufficient payload (model declined to judge)0.0050.000

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 head0.013
GPT teacher head0.284
Teacher spread0.271 · 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

Citations98
Published2010
Admission routes1
Has abstractyes

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