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Record W1802026516 · doi:10.3968/5931

Challenges of Preschool Education Development in Chinese Poverty Counties: Stakeholders' Perspective

2014· article· en· W1802026516 on OpenAlexvenueno aff
Kongchao He, Xiaoping Yang

Bibliographic record

VenueHigher education of social science · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyEconomic growthGovernment (linguistics)ChinaPerspective (graphical)Economic shortagePoliticsPreschool educationPolitical scienceSociologyPedagogyEconomics

Abstract

fetched live from OpenAlex

Due to the historical roots and current political system, preschool education development in China is extremely uneven. At the bottom of preschool education development pyramid, there are 592 national poverty-stricken counties, all of which are in remote and rural areas. From the perspective of stakeholders, based on the interviews with 114 participants from county F which focused on “the challenge for the development of preschool education”, it is concluded that the main challenges for poverty counties in developing preschool education consists of serious shortage of funds, low qualified teachers, unclear functions of government and the crisis of educational quality. It is believed that the central government’s active intervention, the service functions of local government, the communication between kindergarten and family and teachers’ self-development should be the right way to better development in the future.

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)0.002
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 score0.087
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.003
Scholarly communication0.0030.002
Open science0.0010.003
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 head0.036
GPT teacher head0.341
Teacher spread0.305 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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