Well grounded: Indigenous Peoples' knowledge, ethnobiology and sustainability
Bibliographic record
Abstract
Abstract The biological knowledge and associated values and beliefs of Indigenous and other long‐resident Peoples are often overlooked and underrepresented in governance, planning and decision‐making at local, regional, national and international levels. Ethnobiology—the study of the dynamic relationships among peoples, biota and environments—is a field that places Indigenous Peoples' ecological knowledge and ways of knowing at the forefront of research interests, particularly in relation to the importance of biocultural diversity in sustaining the Earth's Ecosystems. In this paper, we examine the nature and significance of Indigenous Peoples' knowledge systems concerning environmental sustainability, as documented in collaborative ethnobiological research. We emphasize the diverse aspects of Indigenous knowledge in conservation, and the role played by ethnobiologists in respectfully highlighting this knowledge, and link these to the Intergovernmental Science‐Policy Platform on Biodiversity and Ecosystem Services Global Assessment's key levers and leverage points for enabling the transformative change required for achieving more sustainable lifeways. Drawing on diverse ways of knowing—respectfully, collaboratively, ethically and reciprocally—can help provide more detailed knowledge of local ecosystems, and guide all humans towards greater sustainability. From environmental monitoring, to building relationships with plants and the land, to ecological restoration, there are many lessons and ways in which the intersections between Indigenous knowledge and ethnobiology can inform and contribute to the future of humanity and other life on earth. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".