Indigenous Participation and the Incorporation of Indigenous Knowledge and Perspectives in Global Environmental Governance Forums: a Systematic Review
Bibliographic record
Abstract
Global environmental governance (GEG) forums, such as those convened through the United Nations, result in the development of monumental guiding frameworks such as the Sustainable Development Goals (SDGs) and the Convention on Biological Diversity (CBD) Conference of Parties (COPs) Aichi and post-2020 targets. The ratification of policy frameworks by member and/or signatory states can result in major shifts in environmental policy and decision-making and has major implications for Indigenous communities. In this article, we present systematic review of the peer-reviewed literature on Indigenous participation in GEG forums, and focus on the specific questions: (1) what GEG forums include Indigenous participation and (2) how do Indigenous peoples participate in GEG forums, including how their perspectives and knowledges are framed and/or included/excluded within governance discussions, decisions, and negotiations. We provide a bibliometric analysis of the articles and derive seven inductively determined themes from our review: (1) Critical governance forums and decisions; (2) inclusion and exclusion of Indigenous voices and knowledge in GEG forums; (3) capacity barriers; (4) knowledge hierarchies: inclusion, integration, and bridging; (5) representation and grouping of Indigenous peoples in GEG; (6) need for networks among and between Indigenous peoples and other governance actors; and (7) Indigenous peoples influence on GEG decisions and processes. Our findings can be used to improve GEG forums by contributing to the development strategies that address the barriers and inequities to meaningful and beneficial Indigenous participation and can contribute to future research that is focused on understanding the experiences of Indigenous peoples within GEG forums.
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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 | 0.024 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.024 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".