Features of Academia-Industry Interactions in Nigeria from the Perspective of Manufacturing Firms
Bibliographic record
Abstract
The contribution of academia to economic development depends on the extent to which firms are able to employ the knowledge they generate. This paper draws from the report of a survey of Nigerian manufacturing firms aimed at ascertaining the level and scope of firms’ interaction with the academia comprising of universities and public research institutes, and their implications for building local technological capability. The results of the study showed that while firms have used existing production processes to manufacture products that are new to Nigeria, R&D capability is still relatively weak. The academia took the least position in the perception of firms as source of knowledge that had resulted in new projects or completion of existing innovative projects. Firms generally perceive the quality of R&D in the universities and research institutes to be low, and hence depend largely on their limited in-house R&D. It thus follows that building local technological capability would require raising the quality of R&D in universities and research institutes, and active promotion of collaborative R&D projects between firms and universities/research institutes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".