Integration of indigenous knowledge with scientific knowledge: A systematic review
Bibliographic record
Abstract
The integration of indigenous knowledge with scientific knowledge has emerged as a key area of interest in various disciplines, including environmental management, agriculture, healthcare, and education. Indigenous knowledge, developed over centuries by Indigenous peoples and local communities, reflects a deep-rooted understanding of local ecosystems, sustainable practices, and holistic approaches to health and development. Meanwhile, scientific knowledge, often seen as more universal and formalized, contributes empirical methodologies and technological advancements. This systematic review explores the importance, challenges, and benefits of integrating these two knowledge systems. By reviewing relevant literature, this paper identifies pathways for successful integration, highlighting case studies from environmental conservation, agriculture, and healthcare that demonstrate the complementary strengths of indigenous and scientific knowledge. The paper concludes that integrating scientific and indigenous knowledge holds great promise for addressing global challenges. Despite obstacles like power disparities and differing epistemologies, effective integration can lead to a comprehensive and lasting solution that promotes equitable collaborations, protects intellectual property, and creates culturally appropriate frameworks. Collaborative research that treats indigenous populations as equal partners ensures innovations are both scientifically and culturally valid. Successful integration therefore requires frameworks sensitive to cultural differences and the social and spiritual aspects of indigenous knowledge, supported by legal and policy measures to safeguard and benefit from indigenous knowledge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".