Quality Administration and Management in Higher Education in Nigeria: Implications for Human Resource Development
Bibliographic record
Abstract
The dynamic changes in today’s world have made countries of the world masters of their own destinies. In this light, it has become noted today that ‘the affluence or penury of nations depends largely on the quality of higher education’. This is informed by the fact that higher education systems of a nation is the ‘machinery of manpower creation’ of the nation and as a result nations have to embrace quality on a continuous basis to be able to be in touch with the realities of today’s change in technological, sociological and economical dimensions. Quality higher education system will produce quality skills and quality human capacity. Therefore, in adjustment to needs for development countries such as Nigeria should embrace and implement Continuous Quality Improvement (CQI), Total Quality Management (TQM) in the universities and Higher Education administration for the purpose of all-round Human Resource Development. TQM and CQI implementation in the university system will go a long way in expanding the skills and capacity of the academic staffs and consequently that of the students. In effect, there will be increase in Human Capital Development across the nation and the attendant economic growth, technological growth, innovation and general Human Resource Development towards National Development. Thus, this article reviewed the literatures on quality administration and management in higher education in Nigeria with the aims of highlighting the implication of human resource development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".