Weaving the Middle Spaces Between Indigenous and Scientific Knowledge for Biodiversity Conservation and Ecology
Bibliographic record
Abstract
ABSTRACT Africa's unparalleled biodiversity and cultural heritage are closely tied to Indigenous Peoples (IP) and their traditional ecological knowledge (TEK) systems, which offers vital insights into conservation and sustainability. This editorial highlights the active role of African IP in biodiversity conservation and food system resilience, emphasising the urgent need to forge equitable partnerships across knowledge systems rather than subordinating TEK to scientific knowledge (SK). TEK, embedded in centuries of observation and cultural practices, informs ecological processes and sustainable resource use. However, climate change, land dispossession and cultural erosion, among other drivers, threaten these knowledge systems and the communities that uphold them. A collaborative approach that respects Indigenous sovereignty can foster interdisciplinary conservation efforts. This aligns with ongoing efforts at the international scene, such as the Kunming–Montreal Global Biodiversity Framework, which explicitly recognises the rights of IP, as well as those of other local communities in multiple conservation targets, including land rights, traditional knowledge and access to justice. The challenge remains: How can these international commitments translate into equitable, rights‐based conservation on the ground? It is crucial to ensure that conservation policy and practice are consistent with the United Nations Declaration on the Rights of Indigenous Peoples and uphold moral responsibility. Equally important is fostering shared interests between conservationists and IP by engaging in open dialogue about conflicts of interest and building trust with Indigenous communities. By integrating these principles, conservation and ecological sciences can move beyond theoretical commitments to genuine, participatory conservation efforts that respect and sustain IP's stewardship of nature.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".