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Market Economy, Social Change, and Education Inequality in China

2018· reference-entry· en· W2924523171 on OpenAlexaff
Shibao Guo, Yan Guo

Bibliographic record

VenueOxford Research Encyclopedia of Education · 2018
Typereference-entry
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMarketizationDecentralizationPlanned economyChinaEconomicsEconomic growthEconomic systemPolitical scienceMarket economy

Abstract

fetched live from OpenAlex

China has experienced major shifts from a centrally planned economy to a market economy, from centralization to decentralization, from state ownership to privatization, and from a decisive state to a weakened state. Despite China’s economic miracle, the country also faces unprecedented challenges, including rising social inequality, rural-urban divide, regional disparity, environmental degradation, declining health and education conditions, and polarization between the rich and poor. China’s profound socioeconomic and political transformations have led to significant fundamental changes to education in China, as manifested in its decentralization, marketization, and privatization. One significant paradigm change relates to the devolution of education power and policy from a centralized governance model to local governments. With the privatization and marketization of its education system, China has adopted a market-oriented approach with the orientation, provision, student enrollment, curriculum, and financing of education. There is sufficient evidence to suggest that there has been a withdrawal of the mighty state from its paternalistic role in the provision and subsidy of public education. Unfortunately, the market economy has further increased education inequalities. The maldistribution of resources and education opportunities raises important questions about issues of social justice and equity regarding who gets how much education as the social good.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.623
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.069
GPT teacher head0.400
Teacher spread0.331 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2018
Admission routes1
Has abstractyes

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