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Record W3159363041 · doi:10.1080/13504622.2021.1896680

Education for sustainability in early childhood education: a systematic review

2021· review· en· W3159363041 on OpenAlexaff
Tülin Güler Yıldız, Naciye Öztürk, Tülay İlhan İyi, Neşe Aşkar, Çağla Banko Bal, Sibel Karabekmez, Şaban Höl

Bibliographic record

VenueEnvironmental Education Research · 2021
Typereview
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsSustainabilityEnvironmental educationQualitative researchSystematic reviewEducational researchEarly childhood educationAction researchPsychologyMedical educationEngineering ethicsPedagogySocial scienceMedicineSociologyPolitical scienceMEDLINEEngineeringEcology

Abstract

fetched live from OpenAlex

This study aims to review the scientific papers on Early Childhood Education for Sustainability (ECEfS) published between 2008 and 2020 and reveal changes in the area. This systematic review was carried out in two stages. In the first stage, a systematic review of papers on ECEfS was conducted according to the specified criteria, and all identified studies were evaluated descriptively. In the second stage, interventional research was evaluated, and their results were reviewed. It was seen that qualitative research methods were mostly preferred in the reviewed studies and most of them were conducted with children. It was determined that the most frequently discussed pillar is environmental. Moreover, the number of interventional research studies is limited. The research findings, it is thought that there is a need for future studies that use interventional, experimental and action research methods, holistically addressing pillars of sustainability.

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)0.012
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.037
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0170.015
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0020.002
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 head0.028
GPT teacher head0.410
Teacher spread0.382 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations83
Published2021
Admission routes1
Has abstractyes

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