Policy Issues in the Administration of Higher Education in Nigeria
Bibliographic record
Abstract
This paper evaluates policy initiatives in the Nigerian higher educational system with a view to bringing it in linewith good practices. Issues of gender, management/governance, teaching, research and funding were discussed. Thestudy revealed that government’s funding is insufficient to maintain institutional performance because of the policystance of non-payment of tuition fees at undergraduate level. It advocates policy reforms that support cost sharingwith students but with appropriate mix of scholarships to enable students pay prescribed fees. The paper indicatedthat participatory governance is appropriate in administering universities because of its capacity to involve relevantstakeholders in decision-making. The paper highlighted that, teaching and research in Nigerian Universities are notresponsive to employers’ requirements; and new policy initiatives geared towards ameliorating the situation arehampered by shortage of staff, inadequate funding and poor physical facilities. The paper found that genderinequality in higher education is a social problem which has necessitated the creation of centres for mainstreaminggender in the system. The study concluded that for Universities and other higher educational institutions in Nigeria toremain self-reliant, self-steering and able to survive in a competitive world; various higher education policies shouldbe effectively institutionalized and operationalised.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.025 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".