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Chinese Higher Education: The role of the economy and Projects 211/985 for system expansion

2020· article· en· W3033468396 on OpenAlexaff
Danilo de Melo Costa, Qiang Zha

Bibliographic record

VenueEnsaio Avaliação e Políticas Públicas em Educação · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsYork University
Fundersnot available
KeywordsBenchmarkingChinaHigher educationExploratory researchGovernment (linguistics)Economic growthQualitative researchChinese economyPerspective (graphical)Political scienceBusinessEconomyEconomicsMarketingSociologySocial science

Abstract

fetched live from OpenAlex

Abstract China has experienced a significant economic growth in recent years. In addition, the country has also built the largest system of Higher Education in the world. However, was the economy that stimulated the advancement of Higher Education? Or was Higher Education that stimulated the advancement of the economy? To answer these questions, this research aimed to understand the role of economy and Projects 211 and 985 for the expansion of Chinese Higher Education. For that, an exploratory and qualitative research was developed, based on interviews with Chinese government managers and questionnaires applied to professors/specialists and to a student leadership. The results showed that investments in Higher Education were preponderant for the country’s economic growth, which was representative from a quantitative perspective. However, also aiming at qualitative growth, projects 211 and 985 were created, allocating a significant amount of resources to the selected institutions. Such positioning makes China an example of benchmarking for other countries that wish to progress economically and intellectually.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.323
Teacher spread0.298 · 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
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

Citations15
Published2020
Admission routes1
Has abstractyes

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