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Record W3135602774 · doi:10.1177/1478210321995650

The political initiative of Taiwan’s education for sustainable development: Looking through the lens of Chinese legalism

2021· article· en· W3135602774 on OpenAlexaff
Ying-Syuan Huang, Anila Asghar

Bibliographic record

VenuePolicy Futures in Education · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsMcGill University
Fundersnot available
KeywordsLegalism (Western philosophy)Education for sustainable developmentPoliticsPublic administrationGovernment (linguistics)Sustainable developmentEnvironmental educationPolitical scienceCorporate governanceSustainabilityEducation policyNegotiationPhilosophy of educationSociologyEnvironmental governanceHigher educationLawManagementEconomics

Abstract

fetched live from OpenAlex

This article examines the development of Taiwan’s Environmental Education Act and how Education for Sustainable Development was mainstreamed into the national policy framework within the country. The goal is to understand the policy tools and governing strategies that were used by the Taiwan government to develop and implement a nationwide environmental education policy for integrating environmental sustainability into all areas and levels of teaching and learning. Official documents related to national plans for ESD and environmental education policies were analyzed and examined. In particular, Chinese Legalism was used as a lens to interpret the government’s philosophy, assumptions, unspoken norms, legislative practices, and deliberate strategies. Several principles and techniques proposed by Chinese Legalists were used to examine the negotiation and formulation of Taiwan’s Environmental Education Act. This analysis contributes to our understanding of the ways in which UNESCO’s framework of ESD can be transferred into a national policy. A discussion of the Chinese Legalist philosophy also offers a cultural frame of reference to think about ESD politics and governance in other East Asian contexts.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.319
Teacher spread0.309 · 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 teacher head, 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

Citations7
Published2021
Admission routes1
Has abstractyes

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