Integrating local and scientific knowledge: The need for decolonising knowledge for conservation and natural resource management
Bibliographic record
Abstract
Integrating Indigenous and local knowledge in conservation and natural resource management (NRM) initiatives is necessary to achieve sustainability, equity, and responsiveness to local realities and needs. Knowledge integration is the starting point for converging different knowledge systems and enabling knowledge co-production. This process is also a key prerequisite towards decolonising the research process. However, power imbalances may perpetuate dominant forms of knowledge over others, obstruct knowledge integration, and eventually cause the loss of knowledge of the marginal and less powerful knowledge holders. Despite increasing interest in knowledge integration for conservation, NRM, and landscape governance, documentation of integration processes remains fragmented and somewhat scarce. This semi-systematic literature review contributes to filling this gap by synthesising methods, procedures, opportunities, and challenges regarding integrating and decolonising knowledge for conservation and NRM in Southern Africa. The findings demonstrate that despite an increasing number of studies seeking to integrate Indigenous and local knowledge and scientific knowledge relevant to conservation and NRM, methods, procedures, and opportunities are poorly and vaguely documented, and challenges and colonial legacies are often overlooked. Documentation, valuing Indigenous and local knowledge, addressing power relations, and collaboration across knowledge systems are missing steps towards efficient knowledge integration. The paper concludes that there is a need for further research and relevant policies. These should address methods and implications for equitable knowledge integration processes and move beyond knowledge sharing and mutual learning towards decolonising knowledge for conservation and NRM.
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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 | 0.048 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.017 | 0.034 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".