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?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".