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Record W2540232739 · doi:10.1080/13504622.2016.1249459

An ESD pathway to quality education in the Cyprus primary education context

2016· article· en· W2540232739 on OpenAlexaff
Chrysanthi Kadji‐Beltran, Nicoletta Christodoulou, Aravella Zachariou, Petra Lindemann‐Matthies, Susan Barker, Costas Kadis

Bibliographic record

VenueEnvironmental Education Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersResearch Promotion Foundation
KeywordsEducation for sustainable developmentCurriculumContext (archaeology)Common groundQuality (philosophy)Environmental educationPedagogySociologyPsychologyMathematics educationSustainable developmentPolitical scienceGeographySocial psychology

Abstract

fetched live from OpenAlex

This research is based on the rationale that the well-defined framework of education for sustainable development (ESD), its connection with real life and its specific integration in the educational policies and curricula can help to enhance quality education (QE) in a meaningful and identifiable way. In a first step, the common ground of ESD and QE was explored in different areas: common dimensions, future-oriented objectives, commonly targeted skills, value orientation, teaching and learning approaches. In a second step, this information was taken as a base to investigate how well twelve lesson units for primary school reflect the common ground of ESD and QE. The units were specifically developed for this research, in which ESD experienced teachers (mentors) supported inexperienced ones (mentees). Results indicate that ESD can reinforce QE, but that teachers need support with regard to the political and cultural dimensions of SD issues, collaborations with local communities and assessments.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.028
GPT teacher head0.398
Teacher spread0.371 · 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 designQualitative
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

Citations14
Published2016
Admission routes1
Has abstractyes

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