An Indigenous perspective on climate engineering
Bibliographic record
Abstract
Indigenous Peoples remain uniquely exposed to the threat of anthropogenic climate change, thereby requiring the research community to collaboratively explore (alongside Indigenous organizations and individuals) new approaches to climate risk mitigation. This Perspective assesses one aspect of climate risk mitigation - climate engineering, an umbrella term which we use to encompass emergent negative emissions technologies (like direct air capture) and research into the spectrum of solar radiation management techniques. In this co-produced contribution from both Indigenous and non-Indigenous collaborators, we situate the research within our community-based Indigenous histories. We then outline the nature of Indigenous climate risk as context for arguing that flawed existing attempts to simplistically assess the nexus of Indigeneity and climate risk management (which can prescriptively provide a universal “Indigenous perspective” across a structurally fragmented, highly complex landscape of Indigeneity) need to be abandoned. We propose methodological and engagement ideas for researchers in this space to consider. Of note, the applied focus of this paper concludes with “next steps” direction based on existing models observed within our fifty years of combined experience at the nexus of Indigeneity and community development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".