Reclaiming fire: resistance, resurgence and Indigenous jurisdiction in the climate emergency
Bibliographic record
Abstract
Climate-amplified emergencies are vital sites for Indigenous resistance and resurgence, even as climate change compounds layers of colonial oppression. Drawing on the experiences of the Tŝilhqot’in Nation before, during and after record-breaking wildfires in the Nation’s territory (in British Columbia, Canada), this article describes how the Tŝilhqot’in employ strategies of resistance to presumed state authority and resurgence of their own laws and jurisdiction in response to the climate emergency. These record-breaking wildfires vividly illustrate how colonial laws and policies have converged over a century to produce the climate emergency. And yet, dominant discourses around climate change and emergency reproduce Indigenous erasure, vulnerability and marginalization. Counter to these discourses, the Tŝilhqot’in Nation has advanced sophisticated emergency responses in relation to the state. Moreover, the wildfires have prompted Tŝilhqot’in communities to revitalize deep-seated fire stewardship laws and responsibilities to properly care for the land, wildlife and each other in the face of the accelerating climate crisis. The Tŝilhqot’in Nation’s experiences reclaiming fire show how Indigenous resurgence provides pathways out of the climate crisis while also attending to its colonial roots and decolonizing the responses to it.
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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 | 0.003 | 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 | 0.026 | 0.042 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".