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Record W4402692541 · doi:10.1080/13504622.2024.2405520

What kind of natural environment picturebooks are young children in China and Korea reading?

2024· article· en· W4402692541 on OpenAlexaff
Tong Tong Kang, So Hyun Jang

Bibliographic record

VenueEnvironmental Education Research · 2024
Typearticle
Languageen
FieldComputer Science
TopicEducational Research and Pedagogy
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsEnvironmental educationReading (process)ChinaPicture booksNatural (archaeology)Early childhood educationPedagogyPsychologySociologyGeographyPolitical scienceLiteratureArt

Abstract

fetched live from OpenAlex

This study identified the number of natural environment picturebooks read by young children in Chinese and Korean picturebook libraries and analyzed their content from an ecocritical perspective. The findings indicate that, first, although the number of natural environment picturebooks read by children is higher in Korea than in China, in both countries, these books make up a small proportion of all books read. Second, the covers and endpapers of these picturebooks concisely represent the stories, stimulating children’s curiosity. Additionally, the analysis using the chronotope showed that natural environment picturebooks enabled children to experience changes in time while emotionally empathizing with the protagonists. This study reveals that natural environment picturebooks read by young children in China and Korea provide cognitive and emotional content but lack in actionable content. Therefore, parental, educational, and societal support is necessary to enhance children’s practices in environmental conservation.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.000
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 head0.018
GPT teacher head0.348
Teacher spread0.330 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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