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Record W4401960677 · doi:10.1080/13504622.2024.2389945

Early childhood education for sustainable development in the Nordic national curricula: status and content

2024· article· en· W4401960677 on OpenAlexaff
Kristín Norðdahl, Hrönn Pálmadóttir, Marianne Presthus Heggen, Nanna Jordt Jørgensen, Ann-Christin Furu, Susanne Thulin, Marie Fridberg, Bente Sandberg, Guri Langholm, Birgitte Damgaard, Janne T. Hirvi, Tejs Møller, Eva Staffans

Bibliographic record

VenueEnvironmental Education Research · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsSustainabilityCurriculumEnvironmental educationEducation for sustainable developmentContent analysisEarly childhood educationPedagogySustainable developmentSociocultural evolutionNational curriculumSociologyQualitative researchCurriculum developmentSustainability organizationsPolitical scienceEngineering ethicsSocial scienceEcologyEngineering

Abstract

fetched live from OpenAlex

This study explores how early childhood education for sustainability (ECEfS) is directly framed in the Nordic curriculum. The study’s theoretical background builds on theories and research viewing ECEfS as a comprehensive approach which integrates the three pillars of sustainability. To explore the status of ECEfS, Nordic curricula were analysed using qualitative content analysis, specifically examining sustainability concepts and the contexts of these concepts in relation to the sustainability pillars. The findings reveal that sustainability education is included in the Nordic national curricula for early childhood education and care (ECEC), but the emphasis varies: ecological and economic sustainability were prioritised over sociocultural sustainability, which is scarcely addressed in some curricula. Incorporating the concept of sustainability into national curricula could encourage teachers to recognise its comprehensive value beyond nature conservation. Additional research is needed to guide the implementation of ECEfS as well as teachers’ understanding of policy documents.

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.002
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.334
Teacher spread0.311 · 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 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

Citations6
Published2024
Admission routes1
Has abstractyes

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