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Record W1835865300 · doi:10.2304/pfie.2005.3.3.5

Influence of the Globalized and Globalizing Sustainable Development Framework on National Policies Related to Environmental Education

2005· article· en· W1835865300 on OpenAlexafffund
Lucie Sauvé, Renée Brunelle, Tom Berryman

Bibliographic record

VenuePolicy Futures in Education · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversité du Québec
FundersUniversité du Québec à Montréal
KeywordsEnvironmental educationSustainable developmentEducation for sustainable developmentPoliticsPolitical scienceSociologyEnvironmental adult educationEconomic growthInternational educationPublic administrationSocial scienceHigher educationPedagogyEconomics

Abstract

fetched live from OpenAlex

This article presents and discusses some results of the authors’ analysis of international and national institutional documents related to environmental education from the 1970s to the present day. The aim of the study is to present a critical characterization of how environmental education is conceptualized and introduced through the ongoing worldwide educational reform movement. The results presented in this article highlight the influence of the globalized and globalizing international political program for sustainable development on national educational proposals. The shift from the previous institutional discourse related to environmental education towards a more explicit economicist view of the world is discussed. The purpose of this article is to stimulate discussion about some of the foundations upon which educational policies and other national initiatives related to environmental education rest, and to introduce elements that could enrich their conceptual and ethical dimensions.

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)0.023
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0050.018
Scholarly communication0.0110.004
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.003
GPT teacher head0.288
Teacher spread0.284 · 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

Citations61
Published2005
Admission routes2
Has abstractyes

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