Indigenous Climate Change Studies: Indigenizing Futures, Decolonizing the Anthropocene
Bibliographic record
Abstract
Indigenous and allied scholars, knowledge keepers, scientists, learners, change-makers, and leaders are creating a field to support Indigenous peoples’ capacities to address anthropogenic (human-caused) climate change. Provisionally, I call it Indigenous climate change studies (Indigenous studies, for short, in this essay). The studies involve many types of work, including Indigenous climate resiliency plans, such as the Salish-Kootenai Tribe’s Climate Change Strategic Plan that includes sections on “Culture” and “Tribal Elder Observations,” policy documents, such as the Inuit Petition expressing “the right to be cold,” conferences, such as “Climate Changed: Reflections on Our Past, Present and Future Situation,” organized by the Indigenous Peoples Climate Change Working Group, and numerous declarations and academic papers, from the Mandaluyong Declaration of the Global Conference on Indigenous Women, Climate Change and REDD to a special issue of the scientific journal Climatic Change devoted to Indigenous peoples in the U.S. context.
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 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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.054 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".