Traditional Ecological Knowledge and the Law Toward Inclusive Environmental Governance
Bibliographic record
Abstract
Traditional ecological knowledge (TEK) has been a key pillar in maintaining the sustainability of ecosystems by indigenous and local communities for centuries. TEK not only reflects a deep understanding of natural cycles and biodiversity, but also contains spiritual, social, and cultural values that shape sustainable resource management practices. However, the existence of TEK is still often marginalized in the formal legal framework that is more dominated by modern Western-based scientific approaches. This article examines the urgency of integrating TEK into national and international legal systems as part of efforts to build inclusive environmental governance. Through a juridical-normative approach and comparative analysis of case studies in Indonesia, Canada, and Brazil, this article highlights the importance of implementing legal pluralism, namely the recognition and coexistence of state law and customary law. It is found that strengthening the legal position of TEK can support environmental conservation, biodiversity protection, and community resilience to climate change. In addition, this article identifies key challenges in the integration of TEK, such as the recognition of indigenous land rights, protection of collective intellectual property, and guaranteeing cultural sovereignty. The discussion is strengthened by an analysis of international legal instruments such as the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) and the Convention on Biological Diversity (CBD), which serve as normative bases in recognizing indigenous peoples’ rights.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.105 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| 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".