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Record W2735368070 · doi:10.26740/jaj.v3n1.p59-69

Menciptakan Mutu Perguruan Tinggi (Higher Educations) Berskala Internasional Melalui Strategi Penerapan Tata Kelola Universitas Yang Baik (Good University Governance)

2011· article· en· W2735368070 on OpenAlexfundno aff
Pujiono Pujiono, Made Dudy Satyawan

Bibliographic record

VenueAKRUAL Jurnal Akuntansi · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Curriculum and Learning Methods
Canadian institutionsnot available
FundersUniversity of Northern British ColumbiaUniversity of PittsburghUniversity of California, Los AngelesYale University
KeywordsBureaucracyAccountabilityCorporate governanceGlobalizationTransparency (behavior)Political scienceSociologyManagementPoliticsEconomicsLaw

Abstract

fetched live from OpenAlex

AbstractThis study sought to strip the university in the era of globalization, and the application of good governance. Globalization has changed the university (colleges) that exist in the world vying to become world class University. Most universities (colleges) in Indonesia have transformed itself from the university for teaching to be research university. Several models of approaches can be adopted to implement good governance in the face of global challenges that can be done through a model or framework of bureaucratic, political, collegial, and symbolic. Besides the university (college) should be able to guarantee transparency, accountability, rensponsive, reponsibility, independency, and fairness as well as other additional principle should be in accordance with the vision and mission of the university.

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)0.002
metaresearch head score (Gemma)0.003
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: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0070.003
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.002

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 head0.066
GPT teacher head0.308
Teacher spread0.242 · 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
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

Citations6
Published2011
Admission routes1
Has abstractyes

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