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Record W2160844739

An Exploration on the Value Appeal and Practice Path of University Governance Under the Context of Good Governance

2015· article· en· W2160844739 on OpenAlexvenueno aff
WU Ye-lin, Wenting Xu

Bibliographic record

VenueCanadian social science · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Identity and Reputation
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceTransparency (behavior)AccountabilityLegitimacyGood governancePublic valueProject governanceContext (archaeology)Value (mathematics)Public administrationAppealPublic relationsPolitical scienceBusinessSociologyEconomicsLawManagementPoliticsComputer science
DOInot available

Abstract

fetched live from OpenAlex

Good governance is a crucial concept in the field of economic and social management. In recent years, it has been increasingly used in the country’s public organization governance, and according to it to explore the governance path of the public sector. University is a knowledge-based public organization. The proposal of university good governance derived from social good governance and state good governance. This article holds that under the context of good governance, the university governance takes justice and efficiency as its value choice. Its legitimacy, rule of law, transparency and other practical appeals will help to promote running a school fairly. At the same time, its accountability, responsiveness and inclusiveness is also important to ensure the generation of efficiency. Based on this, the article further proposes the path to achieve university good governance. That is to build modern university system, strengthen information disclosure, set up dialogue platform, and improve the corporate governance structure and so on.

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)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.023
Scholarly communication0.0130.011
Open science0.0010.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.043
GPT teacher head0.237
Teacher spread0.193 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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