Analysing engagement with Indigenous Peoples in the Intergovernmental Panel on Climate Change’s Sixth Assessment Report
Bibliographic record
Abstract
Abstract Indigenous Peoples’ advocacy and contributions to climate action have drawn international attention, including from the Intergovernmental Panel on Climate Change (IPCC). This article assesses to which degree the IPCC’s Sixth Assessment Report (AR6) recognises the role and knowledge systems of Indigenous Peoples. Through a content analysis of the Working Groups I, II, and III reports and the Synthesis Report, we found an increasing number of references related to Indigenous Peoples and their knowledge systems. However, the IPCC still perpetuates a reductionist approach that reinforces harmful stereotypes. Overcoming this weakness requires greater reflexivity and concrete actions, including consistent recognition of Indigenous Peoples’ rights, refraining from merely portraying Indigenous Peoples as vulnerable and adopting a strengths-based approach, ensuring ethical and equitable application of Indigenous Peoples’ knowledge systems, and involving Indigenous Peoples from the scoping process. By implementing these measures, the IPCC can improve its partnership with Indigenous Peoples in preparation for AR7.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".