Recognising the Nexus Between the Entertainment Industry and Nigeria’s Economic Growth
Bibliographic record
Abstract
The entertainment industry in its present state, as occasioned by the new wave of technology, is a ‘cash cow’. However, most African countries, Nigeria inclusive, are not fully tapping into its great potential to generate the required revenue for their economic growth and development. Anchored on the premise of cultural history, the paper adopts the historical research method to interrogate the concept of entertainment from the pre-colonial era to its current state and find the nexus between entertainment and the economic growth of nations. It finds that the entertainment industry is a gold mine that is not fully explored and concludes that Nigeria needs to capitalise on the new forms of technological advancement that could enhance and boost her economy. It recommends that young people need to be trained and repositioned in the process of driving modern entertainment and creative industries. Future researches need to explore ways Nigerians and Africans will learn to harness and absorb the positive changes in technological advancement and the digital revolution that is fast changing the face and pace of entertainment and the creative industries. This paper was limited by the shortage of schorly documentation on the emerging Nigerian creative industries.
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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 | 0.000 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".