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Record W2582134689 · doi:10.5070/b86110043

Educating Competitive Students for a Competitive Nation: Why and How Has the Chinese Discourse of Competition in Education Rapidly Changed Within Three Decades?

2016· article· en· W2582134689 on OpenAlexaff
Xu Zhao

Bibliographic record

VenueBerkeley Review of Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Reforms and Inequalities
Canadian institutionsUniversity of Calgary
FundersResearch England
KeywordsCompetition (biology)ChinaGovernment (linguistics)Chinese educationPolitical sciencePerceptionEconomic growthSociologyPsychologyEconomics

Abstract

fetched live from OpenAlex

In the late 1980s, the Chinese government instituted massive educational reforms to promote competition between schools and between students. By the late 1990s, however, educational reforms shifted to regulating and reducing competition in primary and secondary education. Why did a rapid policy swing occur? What was the rationale for the policy change? This article examines the Chinese discourse of competition in education by presenting a textual analysis of 101 commentary articles published by Chinese educators between 1986 and 2014. It reports two different views of competition among Chinese educators, one of which strongly prevailed throughout the 28 years. It also documents historical change in the authors’ perceptions of competition: in the late 1980s, as a powerful solution to the educational and social problems facing China, and, by the late 1990s, as a major educational problem itself.

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)0.006
metaresearch head score (Gemma)0.005
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 score0.110
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0100.023
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0030.005
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 head0.044
GPT teacher head0.392
Teacher spread0.348 · 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

Citations19
Published2016
Admission routes1
Has abstractyes

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