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The Fairness of School Education in China and Its Countermeasures

2023· article· en· W4388830666 on OpenAlexaff
Jintao Ge

Bibliographic record

VenueLecture Notes in Education Psychology and Public Media · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Vocational Training
Canadian institutionsYorkville University
Fundersnot available
KeywordsEquity (law)Educational equityChinaInequalityEconomic growthEducational inequalityRural areaEducational resourcesDistribution (mathematics)Political scienceSociologyEconomicsPedagogy

Abstract

fetched live from OpenAlex

This paper studies the background of China’s educational equity and the problems and solutions of Chinese school education. Using the method of literature review, the research results of scholars at home and abroad are integrated, and the field is systematically sorted out and analyzed. The study found that the problems faced by Chinese education mainly include the inequality of regional education development and the inequality of urban and rural education. There are differences in the allocation of educational resources in different regions, resulting in insufficient education quality and opportunities in some regions, which in turn exacerbates the problem of educational inequality. Secondly, the inequality of urban and rural education is also a major problem of education fairness in our China. Due to the uneven distribution of urban and rural economic development and resources, rural education conditions are relatively poor, and students have limited learning opportunities and resources, resulting in a widening urban-rural education gap. country place. In response to these problems, this study proposes solutions to deepen reform and promote educational equity. Through literature review and analysis of the problems existing in our Chinese education, put forward corresponding solutions, and make corresponding efforts to promote educational equity.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.445
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.423
Teacher spread0.368 · 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 teacher head, 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
Published2023
Admission routes1
Has abstractyes

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