Federal Government Funding of Research in Universities in Nigeria, the University of Benin as a Case Study
Bibliographic record
Abstract
It is increasingly evident that research is extremely critical and important if universities are to serve as engines of development in their areas of locations. For a knowledge-driven world, investment in research and development (R&D) is a sine qua non for a nation. Few studies have examined the federal government’s investment in research in her universities. Furthermore, there is no available evidence of studies on the federal government funding of teaching and research equipment in universities in Nigeria. This study, therefore, investigated the federal government funding of research, teaching and research equipment at the University of Benin. Four research questions were posed to guide the study. The findings showed that less than 5% of the total recurrent revenue was allocated for research at the University of Benin during the 1992/93 to 1996/97 academic sessions. The findings indicated that the federal government is not making a robust investment in research and therefore Nigeria is not developing. Hence its economic quagmire. This paper, therefore, canvassed for the revitalization of research in universities in Nigeria as a means of fast-tracking its economic development.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".