Biocultural well-being: Indigenous Peoples’ values for conservation and equity
Bibliographic record
Abstract
The IPBES Values Assessment found that policy making has often ignored the nature-related values of Indigenous Peoples’ and local communities’ (IP and LCs). It identified lack of understanding of IP and LC notions of value in different contexts and for different sociodemographic groups as key knowledge gaps. We explore how the well-being concepts, worldviews, values, customary laws, and traditional livelihoods of different IP and LCs contribute to nature conservation and equity and how they are recognized by different community actors. Case studies were conducted with Quechua peoples in Lares, Cusco, Peru (which contributed to the IPBES Values Assessment); Mijikenda in Rabai, coastal Kenya; Lepcha and Limbu in northeast India; and Naxi and Moso in Yunnan, China. The study used a decolonizing action-research approach and a biocultural systems framework to contribute directly to establishing biocultural territories and reaffirm Indigenous values through the process. It found similar core values of balance, reciprocity, solidarity, and collectiveness with nature and in society across the different cultures, and similar holistic well-being concepts requiring balance between the human, wild, and sacred worlds. The study reaffirms the IPBES Values Assessment’s conceptualization of nature-related values and identifies a fifth type, “under nature,” where humans are governed by nature. It shows that Indigenous values protect wildlife in sacred sites and promote sustainable and equitable resource use, resilience, food security, and nutrition, providing strategies for effective and equitable conservation and achieving the Sustainable Development Goals. However, Indigenous values have significantly weakened particularly in less remote communities in China and India and in coastal Kenya among youth, and some practices marginalize women, likely influenced by colonial patriarchy. The Potato Park biocultural territory and decolonizing action-research approach can be scaled out to different contexts to revitalize Indigenous values, reduce power imbalances, and enhance recognition of diverse values in policy making.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".