The role of artificial intelligence in cataloguing and analyzing indigenous knowledge in Nigeria
Bibliographic record
Abstract
The preservation and utilization of indigenous knowledge (IK) are critical for sustainable development, cultural heritage conservation, and addressing contemporary challenges such as food security, healthcare, and environmental sustainability. In Nigeria, a nation with over 250 ethnic groups and a rich tapestry of indigenous traditions, the oral and localized nature of this knowledge poses significant challenges to its documentation and accessibility. This paper explores the transformative role of artificial intelligence (AI) in cataloguing and analyzing indigenous knowledge, focusing on Nigeria. AI technologies, including machine learning, natural language processing (NLP), and computer vision, offer innovative solutions for digitizing oral histories, analyzing traditional practices, and creating dynamic repositories of indigenous knowledge. These tools can process vast amounts of unstructured data, identify patterns, and generate insights that bridge the gap between traditional wisdom and modern scientific approaches. However, the integration of AI in this domain is not without challenges. Ethical considerations, such as data privacy, intellectual property rights, and community involvement, must be addressed to ensure the respectful and equitable use of indigenous knowledge. This paper highlights the potential of AI to preserve Nigeria’s indigenous knowledge while emphasizing the need for localized AI tools, capacity building, and interdisciplinary collaboration. By adopting community-centered approaches and ethical frameworks, Nigeria can harness AI to safeguard its cultural heritage and leverage Indigenous knowledge for sustainable development. The paper concludes with recommendations for policymakers, researchers, and stakeholders to ensure the responsible and effective use of AI in this context.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".