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Record W2963738883 · doi:10.5539/elt.v12n8p95

The Quality of Play Center Activities of Early Childhood Education in China

2019· article· en· W2963738883 on OpenAlexvenueno aff
Xiaolin Liu, Biying Hu, Jian-Hao Huang

Bibliographic record

VenueEnglish Language Teaching · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
FundersUniversidade de MacauMinistry of Education of the People's Republic of China
KeywordsPsychologyChinaEarly childhoodEarly childhood educationCenter (category theory)Scale (ratio)Quality (philosophy)Rating scaleRural areaSpace (punctuation)Developmental psychologyMedical educationGeographyCartographyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

As one typical form of play, play center activities are commonly used in early childhood education programs in China. This study used the Activities Sub-scale of the Chinese Early Childhood Environment Rating Scale (CECERS) to measure the quality of the play center activities in K2 (4-5 years old) classroom, which based on a stratified random selection of 48 classrooms in Guangdong Province in China. Results found that the overall quality of play center activities was slightly below qualified (5 points), and there are significant differences between urban and rural, private and public, high and low level of kindergartens. Quality of three dimensions (material and space, opportunity and time, design and organization) also showed a medium level (above 4 points), and significant differences of three dimensions are found between urban and rural, private and public, high and low level of kindergartens. Findings and implications will help teachers to better support and enhance children’s play center activities and early learning.

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)0.002
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 score0.088
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.308
Teacher spread0.301 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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