Scientists' Warning to Humanity on Threats to Indigenous and Local Knowledge Systems
Bibliographic record
Abstract
The knowledge systems and practices of Indigenous Peoples and local communities play critical roles in safeguarding the biological and cultural diversity of our planet. Globalization, government policies, capitalism, colonialism, and other rapid social-ecological changes threaten the relationships between Indigenous Peoples and local communities and their environments, thereby challenging the continuity and dynamism of Indigenous and Local Knowledge (ILK). In this article, we contribute to the “World Scientists' Warning to Humanity,” issued by the Alliance of World Scientists, by exploring opportunities for sustaining ILK systems on behalf of the future stewardship of our planet. Our warning raises the alarm about the pervasive and ubiquitous erosion of knowledge and practice and the social and ecological consequences of this erosion. While ILK systems can be adaptable and resilient, the foundations of these knowledge systems are compromised by ongoing suppression, misrepresentation, appropriation, assimilation, disconnection, and destruction of biocultural heritage. Three case studies illustrate these processes and how protecting ILK is central to biocultural conservation. We conclude with 15 recommendations that call for the recognition and support of Indigenous Peoples and local communities and their knowledge systems. Enacting these recommendations will entail a transformative and sustained shift in how ILK systems, their knowledge holders, and their multiple expressions in lands and waters are recognized, affirmed, and valued. We appeal for urgent action to support the efforts of Indigenous Peoples and local communities around the world to maintain their knowledge systems, languages, stewardship rights, ties to lands and waters, and the biocultural integrity of their territories—on which we all depend.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.063 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.043 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.035 | 0.047 |
| Insufficient payload (model declined to judge) | 0.003 | 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".