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Record W4404352630 · doi:10.1007/978-981-97-7415-9

Education in China and the World

2024· book· en· W4404352630 on OpenAlexfundno aff

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicGlobal Educational Reforms and Inequalities
Canadian institutionsnot available
FundersDivision of Undergraduate EducationStanford Bio-XUC Berkeley College of ChemistryUniversity of North Carolina at Chapel HillUniversity of Illinois at Urbana-ChampaignInstitute of Education SciencesNational Ethnic Affairs Commission of the People's Republic of ChinaNanjing UniversityPeking UniversityKorea Advanced Institute of Science and TechnologyHokkaido UniversityChongqing University of EducationShanghai Jiao Tong UniversityNational Development and Reform CommissionZhejiang UniversityRenmin University of ChinaTsinghua UniversityMinistry of Education, IndiaNorthwestern UniversityYork UniversityYonsei UniversityUniversity of California, Los AngelesChongqing UniversityUniversity of OxfordUniversity College LondonUniversity of California, San DiegoYale UniversitySeoul National UniversityStrongUniversity of ChicagoLondon School of Economics and Political ScienceUniversity of WashingtonPrinceton UniversityJohns Hopkins UniversityUniversity of Wisconsin-MadisonBrown UniversityMinistry of Education, Culture, Sports, Science and TechnologyHarvard UniversityCalifornia Institute of TechnologyUniversity of PennsylvaniaUniversity of Science and Technology of ChinaHebei Normal UniversityNanyang Technological UniversityImperial College LondonMassachusetts Institute of Technology
KeywordsChinaPolitical scienceGeographyArchaeology

Abstract

fetched live from OpenAlex

This open access book builds upon the commitment to provide a comprehensive analysis on education in China to a global audience.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Other
Teacher disagreement score0.156
Threshold uncertainty score0.310

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0040.003
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.013
GPT teacher head0.340
Teacher spread0.327 · 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 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

Citations5
Published2024
Admission routes1
Has abstractno

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