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Record W2119341256 · doi:10.3390/su5083562

Alternative Communications about Sustainability Education

2013· article· en· W2119341256 on OpenAlexaff
Sue L. T. McGregor

Bibliographic record

VenueSustainability · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsPremiseSustainabilityConceptualizationNormativeVanguardEngineering ethicsSustainable developmentSociologyEnvironmental ethicsManagement sciencePolitical scienceEcologyEpistemologyComputer scienceEngineeringArtificial intelligenceGeographyLaw

Abstract

fetched live from OpenAlex

In preparation for the UN Decade of Education for Sustainable Development, UNESCO communicated its conceptualization of education for sustainable development (ESD). This paper does not assume that UNESCO was ineffective in communicating its approach to ESD; rather, the premise is that UNESCO’s actual message was not well received by everyone, with some pushing back with alternative communications of their own. This paper identifies and profiles seven vanguard theoretical and pedagogical approaches to the problem of unsustainability, including, but not limited to: sustainable contraction, unlearning unsustainability, a 3D-heuristic, an integrative, place-based approach, and a Gaia-informed, ecological approach. It concludes with a discussion of seven overarching alternative messages for communicating about sustainability including: refocused education; complexity, chaos and living systems; Gaia and ecology; paradigm shifts for uncertainty; knowledge integration; existentialism; and fear and hope. Intellectual and pedagogical discourse can be kindled and stimulated by drawing on alternative communications about the normative concept of sustainability.

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)0.020
metaresearch head score (Gemma)0.047
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.020
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.047
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0100.025
Scholarly communication0.0130.020
Open science0.0020.011
Research integrity0.0110.015
Insufficient payload (model declined to judge)0.0110.001

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 head0.007
GPT teacher head0.289
Teacher spread0.282 · 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
GenreCommentary

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

Citations16
Published2013
Admission routes1
Has abstractyes

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