Fostering Technological Entrepreneurship for Socioeconomic Development: A Case for Technology Incubation in Bayelsa State, Nigeria
Bibliographic record
Abstract
There is an observed dearth of technological entrepreneurship across Africa, worse still, the Niger Delta region (NDR) of Nigeria. Entrepreneurship generally and technological entrepreneurship in particular are now considered as the engine for economic development. Technology-business incubators (TBI) are seen as a means of tackling developmental challenges. Most developed and emerging economies and developing countries have adopted TBI to fast-track the creation of new technology-based enterprises because of its more than 80% success rate of new venture creation, and have consequently benefited from its multiplier effects such as technology/knowledge transfer, employment generation and wealth creation. In the light of lack of data on incubation activities in Africa, more so for Nigeria, the paper highlights the contributions of TBIs to regional development. This to draw attention of stakeholders in the Yenagoa Technology Incubation Centre which will contribute to the socioeconomic and technological development of the State and NDR, by promoting technological entrepreneurship, eradicating poverty, enhancing Nigeria’s technological capability, thereby ultimately reducing her reliance on petroleum resources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.005 |
| 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".