MétaCan
Menu
Back to cohort
Record W2912138240 · doi:10.1108/jarhe-09-2018-0200

The internal governance model in Chinese universities: an international comparative analysis

2019· article· en· W2912138240 on OpenAlexaffabout
Wei Liu, Weigang Yan

Bibliographic record

VenueJournal of Applied Research in Higher Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAutonomyCorporate governanceOriginalityChinaGovernment (linguistics)Higher educationPublic relationsPolitical scienceSociologyPublic administrationManagementQualitative researchSocial science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to glean a comprehensive picture of the internal governance structure in Chinese universities based on data from 40 university administrators from 33 Chinese institutions. Design/methodology/approach The 40 administrators were convenience sampled while they were taking a three-month higher education leadership development program in a large public university in Canada. Permission was obtained to use the comparative discussions at different reflective research sessions as data to inform this study. The data were also progressively collected through informal interviews throughout the three months. Findings The study finds that the current governance model practiced in Chinese universities can be called “administrator governance,” with all members on the two major governing bodies being senior administrators appointed by and accountable for the governments. To build a “modern university system” aspired in China, the Chinese university administrators perceived a need to strengthen institutional autonomy and collegial governance with participation of the faculty and students. Originality/value As much of the literature has focused on the government–university relationship in China, this study aims to glean a comprehensive picture of the internal governance structure in Chinese universities.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.067
GPT teacher head0.452
Teacher spread0.384 · 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

Citations16
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Applied Research in Higher EducationSame topicHigher Education Governance and DevelopmentFrench-language works237,207