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Record W2089294973 · doi:10.2167/irg205.0

Global to Local: International Conferences and Environmental Education in The People’s Republic of China

2007· article· en· W2089294973 on OpenAlexaff
Marie-Claude Roch, Kenneth E. Wilkening, Paul Hart

Bibliographic record

VenueInternational Research in Geographical and Environmental Education · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of ReginaUniversity of Northern British Columbia
Fundersnot available
KeywordsChinaContent analysisConsistency (knowledge bases)Political scienceCivil societyInternational educationPublic relationsPublic administrationSociologyHigher educationSocial scienceLaw

Abstract

fetched live from OpenAlex

This paper describes research exploring the relationship between international recommendations for environmental education (EE) developed at United Nations (UN) Conferences and non-formal EE initiatives undertaken by Chinese civil organisations. Specifically, the research assesses how international recommendations may have influenced the development and the delivery of Chinese non-formal EE programmes. Data were collected and analysed through an interpretivist perspective and involved: (1) historical and content analysis of recommendations from UN-sponsored conferences related to EE; (2) interviews with academics, Chinese officials and Chinese civil organisations representatives; and (3) document analysis of academic literature as well as governmental and non-governmental publications related to EE. Findings were interpreted in terms of patterns of influence and consistency. Results show that international recommendations for EE did not directly influence non-formal EE efforts in China; nevertheless, some of these efforts seem consistent with international recommendations. Based on these results, questions are raised about the nature of communication between participants in international deliberations and practitioners at the local level. Suggestions are made for improving this communication.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.343
Teacher spread0.331 · 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 designObservational
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
Published2007
Admission routes1
Has abstractyes

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