Management Strategies for Improving the Functionality of Tertiary Education in Nigeria
Bibliographic record
Abstract
This study investigates management strategies for improving the functionality of tertiary education in Nigeria. One research question was asked and answered and three hypotheses were formulated and tested at 0.05 alpha level. The sample of the study comprised 900 respondents selected through the simple random sampling technique from six tertiary institutions in Delta and Edo States of Nigeria. The questionnaire was the instrument for collection of data from the respondents. Descriptive statistics in the forms of mean and standard deviation were used to answer the only research question. The three hypotheses were tested using one way analysis of variance. The results obtained showed that improved funding, monitoring and adoption of best practice in service delivery would help to improve the functionality of tertiary education in Delta and Edo States. Staffs of University, Polytechnics and Colleges of Education did not differ significantly in their mean perception scores for any of the identified management strategy for improving the functionality of tertiary education. The study recommended that governments of Delta and Edo States should give adequate priority to tertiary education by ensuring that enough fund is allocated and disbursed to the institutions for proper management of affairs and improved functionality.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".