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Record W2146036197 · doi:10.5430/ijhe.v3n2p38

Governance and Higher Education in Pakistan: What Roles do Boards of Governors Play in Ensuring the Academic Quality Maintenance in Public Universities versus Private Universities in Pakistan?

2014· article· en· W2146036197 on OpenAlexvenueno aff
Sidra Usman

Bibliographic record

VenueInternational Journal of Higher Education · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsHigher educationCorporate governanceAutonomyPublic sectorPrivate sectorBusinessQuality (philosophy)Public administrationAccreditationCommissionPublic relationsEconomic growthPolitical scienceEconomicsFinance

Abstract

fetched live from OpenAlex

There are major structural issues in the higher education system in Pakistan leading to poor governance of institutions and questionable quality of education. This paper looks at the differences in the role of boards of governors in maintaining quality of education in both the public and the private sector universities in Pakistan. After having conducted qualitative research by interviewing selected academics and by secondary research, the problems in the governance of higher education in the country were identified. Governance in higher education is then analyzed in terms of the Boards of Governors of universities and their role and the overall management and organizational structure of the higher education institutions in both sectors. This paper further explores the role of the Higher Education Commission of Pakistan as a regulator of higher education in the country and its policies regarding quality assurance. This discussion helps in identifying the differences that are present in the governance structures of universities in both sectors. The result has been that the private sector boards portray a more efficient system compared to the public sector boards that lack autonomy and are under strong political influence. The recommendations that have been made require for a change in the organizational structures of boards in the public sector and increased checks by the HEC to promote good governance and quality assurance in universities in the country.

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)0.003
metaresearch head score (Gemma)0.007
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: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0040.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.367
Teacher spread0.348 · 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

Citations45
Published2014
Admission routes1
Has abstractyes

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