MétaCan
Menu
Back to cohort
Record W2904185367 · doi:10.3968/10649

The Regression Analysis of the Kindergarten Teachers' Education Concept

2018· article· en· W2904185367 on OpenAlexvenueno aff
Mao Le, Bin Zeng, Yang Li

Bibliographic record

VenueCanadian social science · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Methods and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMathematics educationRank (graph theory)Educational attainmentTask (project management)Early childhood educationPreschool educationProfessional developmentPedagogyMathematics

Abstract

fetched live from OpenAlex

Scientific and rational concept of education is of great significance to the development of kindergarten teachers. In order to investigate kindergarten teachers’ concept of education task, education content, young children’s one-day life, and young children’s active learning. The Kindergarten Teachers’ Education Concept Questionnaire was adopted in the research, and 138 kindergarten teachers who come from Sichuan province were sampled as the research objects. The results showed that educational attainment had an overall effect on kindergarten teachers’ education concept. Professional rank had an effect on kindergarten teachers’ concept of education task. Kindergarten teachers’ concept of education content had significant differences in different regions. Household income had an effect on kindergarten teachers’ concept of young children’s one-day life. Personal relations had an effect on kindergarten teachers’ concept of children’s active learning. That is to say, kindergarten teachers’ education concept was influenced by factors like educational attainment, professional rank, location of kindergarten, personal relations, household income etc.

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)0.004
metaresearch head score (Gemma)0.017
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 score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.038
GPT teacher head0.436
Teacher spread0.397 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueCanadian social scienceSame topicEducation Methods and PracticesFrench-language works237,207