Beyond 'Nollywood' and Piracy: In Search of an Intellectual Property Policy for Nigeria
Bibliographic record
Abstract
Bio and information communication technologies have created a new global economy and have helped in re-shaping the competitive profile of many developing countries in the new knowledge economy. In Nigeria, because of the abundance of creative cultural energy and investments in capacity building in the arts in the late 20th century, there has been a quickened uptake of digital technologies in the movie industry that has since given rise to what is known as As creature of technological conversion, Nollywood has made Nigeria one of the top three movie producing nations in the world. Stakeholders in Nigeria's movie industry have constituted into a powerful pressure group. Along with the Nigerian Copyright Commission (NCC) and other actors, they have managed to make fighting piracy a central thrust of Nigeria's intellectual property policy. While the anti-piracy zeal of the NCC has found favour with external interests, it is doubtful if a narrow intellectual property policy, one that focuses on piracy alone, is capable of articulating Nigeria's other interests in areas such as such traditional knowledge, agriculture and various creative repertoire in that country. There is an urgent need for a more constructive and comprehensive approach for intellectual property policy, one that articulates Nigeria's interest as an important regional power and a developing country in the global knowledge economy. The author calls for a purposeful intervention that brings more stakeholders into IP policy creation, and incorporates linked issues such as food security, farmers' rights, traditional medicine, environmental protection and overall empowerment of traditional knowledge and its stewards. Along with the NCC, the National Office for Technology Acquisition and Promotion, the Patent and Trademark Registry, the academia and the legal profession need to forge an alliance of purpose toward the fashioning and implementing of a more comprehensive IP policy for Nigeria, beyond Nollywood.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".