Pursuing an Indigenous Platform: Exploring Opportunities and Constraints for Indigenous Participation in the UNFCCC
Bibliographic record
Abstract
Despite growing consensus that Indigenous peoples, knowledge systems, rights and solutions should be meaningfully included in international climate change governance, substantive improvements in practice remain limited. An expanding body of scholarship examines the evolving discursive space in which issues facing Indigenous peoples are treated, with a predominant focus on decision outcomes of the United Nations Framework on Climate Change (UNFCCC). To understand the opportunities and constraints for meaningful participation of Indigenous peoples in international climate policy making, this article examines the experiences of Indigenous participants in the UNFCCC. We present findings from semistructured interviews with key informants, showing that material constraints and the designation of Indigenous peoples as nonstate observers continue to pose challenges for participants. Tokenism and a lack of meaningful recognition further constrain participation. Nevertheless, networks of resource sharing, coordination, and support organized among Indigenous delegates alleviate some of the impacts of constraints. Additionally, multistakeholder alliances and access to presidencies and high-level state delegates provide opportunities for international and national agenda-setting. The space available for Indigenous participation in the UNFCCC is larger than formal rules dictate but depends on personal relationships and political will. As the Local Communities and Indigenous Peoples Platform established by the Paris Agreement formalizes a distinct space for Indigenous participants in the UNFCCC, this article outlines existing opportunities and constraints and considers potential interactions between the evolving platform and existing mechanisms for participation.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.020 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".