Making room for meaningful inclusion of Indigenous and local knowledge in global assessments: our experiences in the values assessment of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services
Bibliographic record
Abstract
In recognizing the urgent need to address global challenges such as biodiversity loss, food insecurity, and climate change, it is essential to incorporate diverse knowledge systems, including Indigenous worldviews and knowledge of nature. The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) has taken significant steps to integrate Indigenous and local knowledge (ILK) as well as the viewpoints of Indigenous peoples and local communities (IPLC) into its thematic and methodological assessments. This inclusive approach enriches our understanding of nature and enhances our ability to tackle these pressing global issues. The recently published IPBES Report on Diverse Values and Valuation of Nature, also known as the values assessment (VA), includes Indigenous scholars and ILK experts as authors. The VA provides an interdisciplinary synthesis of existing knowledge on the various ways in which humans value nature, as well as the methods and approaches for understanding these values. It also examines the extent to which these values are integrated into decision-making structures and processes. We are a group of Indigenous scholars and ILK experts from the Global South who participated in the VA, specifically in Chapter 3’s “ILK Team.” The value of including IPLC in knowledge-synthesis initiatives is highlighted by our experiences. There are opportunities to improve the inclusion of ILK in similar assessments. The lessons we learned while working at the VA have motivated us to recommend that future assessments and similar initiatives should actively involve IPLC, their knowledge systems, and their ancestral wisdom.
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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 | 0.073 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.029 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".