Working with indigenous, local and scientific knowledge in assessments of nature and nature's linkages with people
Bibliographic record
Abstract
Working with indigenous and local knowledge (ILK) is vital for inclusive assessments of nature and nature’s linkages with people. Indigenous peoples’ concepts about what constitutes sustainability, for example, differ markedly from dominant sustainability discourses. The Intergovernmental Platform on Biodiversity and Ecosystems Services (IPBES) is promoting dialogue across different knowledge systems globally. In 2017, member states of IPBES adopted an ILK Approach including: procedures for assessments of nature and nature’s linkages with people; a participatory mechanism; and institutional arrangements for including indigenous peoples and local communities. We present this Approach and how it supports ILK in IPBES assessments through: respecting rights; supporting care and mutuality; strengthening communities and their knowledge systems; and supporting knowledge exchange. Customary institutions that ensure the integrity of ILK, dialogues, and shared governance are among critical capacities that enable inclusion of diverse conceptualizations of sustainability in assessments.
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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 | 0.029 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".