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Record W2994880920 · doi:10.5539/ies.v13n1p21

Inclusive Early Childhood Settings: Analyses of the Experiences of Thai Early Childhood Teachers

2019· article· en· W2994880920 on OpenAlexvenueno aff
Sunanta Klibthong, Joseph Seyram Agbenyega

Bibliographic record

VenueInternational Education Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsnot available
FundersMahidol University
KeywordsEarly childhoodEarly childhood educationWorkloadPsychologyProfessional developmentWork (physics)Inclusion (mineral)Faculty developmentPedagogyDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

Findings from child development research support inclusive practice in early childhood education to enable full participation of all children in learning activities and build their core capabilities for life. However, the implementation of inclusive practices in early childhood is often constrained by boundary-crossing barriers. This paper reports a quantitative study that investigated and analysed the inclusive practice experiences of 344 pre-school teachers across the six regions of Thailand. The study identified positive experiences of inclusive practices linked to effective collaboration with minimal barriers related to time, increased workload and lack of resources to help teachers cater to the needs of all children. The findings offer direction for developing teachers as leaders to work effectively across professional boundaries so that Thailand can achieve the goals of inclusive education for all children.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.003
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.002
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 head0.055
GPT teacher head0.458
Teacher spread0.403 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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