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Record W4390192710 · doi:10.5206/cie-eci.v52i2.16930

Environmental Education in Vietnam

2023· article· en· W4390192710 on OpenAlexaffvenue
Tien Thang Pham

Bibliographic record

VenueComparative and International Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in Asia
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPerformative utteranceIdeologySociocultural evolutionNeglectCurriculumContext (archaeology)SustainabilityEducation policyPolitical scienceEnvironmental educationSociologyPolitical economyEconomic growthSocial sciencePoliticsPedagogyGeographyHigher educationEconomicsEpistemologyPsychologyEcologyAnthropology

Abstract

fetched live from OpenAlex

This article utilizes critical discourse analysis to investigate the environmental education (EE) policy in Vietnam by analyzing seven policy documents. It examines the language used in the policy papers and underlying factors that shape the policy, including power relations, ideologies, and sociopolitical influences. The findings reveal a lack of shared understanding of EE and the neglect of its social aspect, leading to limited integration of EE content in national curricula and the prevalence of performative language within the policy. This undervaluation of EE can be attributed to the influence of Confucianism and the traditional education model inherited from the former Soviet Union. It also suggests that the present policy predominantly reflects the ideologies and priorities of the ruling class, who prioritizes economic growth over sustainability. This study underscores the need for a nuanced approach in policymaking that integrates sociocultural dimensions and addresses the performative gap in environmental education, paving the way for more effective and context-sensitive strategies in Vietnam’s response to educational challenges posed by climate change.

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)0.002
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
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 head0.066
GPT teacher head0.417
Teacher spread0.351 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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