Bibliographic record
Abstract
Abstract This article describes, assesses, and explains the growing status of indigenous knowledges (IKs) in climate science and politics. Informed by a critical environmental perspective we review the literature on traditional ecological knowledge (TEK), explore the contested nature of this concept, and identify the numerous epistemological obstacles to the appropriate and respectful inclusion of traditional ecological knowledge. While we believe that TEK and Western science are complementary, the inclusion of TEK in climate science and politics has been uneven. In support of our argument, we present a framework for assessment of degrees of inclusion of TEK and apply the framework to the United Nations Framework Convention on Climate Change (UNFCCC), the Kyoto Protocol, the Intergovernmental Panel on Climate Change's Fourth Assessment Report (AR4), and the Arctic Climate Impact Assessment (ACIA). We find that the UNFCCC and the Kyoto Protocol do not account for either indigenous peoples or indigenous people's knowledges. The AR4 includes some references to indigenous peoples but they are often buried in regional chapters. The ACIA is the most inclusive of all the documents examined and represents an important starting point for the inclusion of IKs. Based on the findings of our assessment, we conclude with recommendations for moving forward with greater inclusion of IKs. WIREs Clim Change 2011 DOI: 10.1002/wcc.185 This article is categorized under: Social Status of Climate Change Knowledge > Sociology/Anthropology of Climate Knowledge
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".