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Record W2980688385 · doi:10.1177/0973408219872066

Examining Children’s Indoor and Outdoor Nature Exposures and Nature-related Pedagogic Approaches of Teachers at Two Reggio-Emilia Preschools in Halifax, Canada

2019· article· en· W2980688385 on OpenAlexaboutno aff
Nazanin Omidvar, Tarah Wright, Karen Beazley, Daniel Séguin

Bibliographic record

VenueJournal of Education for Sustainable Development · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
Fundersnot available
KeywordsAnthropocentrismEnvironmental educationEarly childhood educationOutdoor educationEarly childhoodCurriculumPedagogyVariety (cybernetics)Education for sustainable developmentCurriculum studiesSociology of EducationPsychologySociologySustainable developmentDevelopmental psychologyPolitical science

Abstract

fetched live from OpenAlex

Early childhood environmental education focusses on expanding children’s bio-affinity, developing their environmental attitudes and encouraging them to behave in a more environmentally friendly manner. One example of the educational method that is based on high-quality practices of both early childhood education and environmental education is the Reggio-Emilia pedagogical approach, which provides children with various nature-related experiences. This study examines the frequency and variety of indoor and outdoor nature experiences for children and the preschool teachers’ educational approaches and goals for children’s development in nature in two Reggio-Emilia preschools located in Halifax, Canada. To do this, first-hand observations and semi-structured teacher interviews were used. Results suggest that the Reggio-Emilia curriculum followed at the preschools provided various opportunities for children to be exposed to nature. However, the teachers have emphasized more on anthropocentric than nature-related educational goals.

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)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0090.002
Scholarly communication0.0020.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.249
Teacher spread0.237 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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