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Record W7119042633 · doi:10.4337/jhre.2025.0007

Reclaiming fire: resistance, resurgence and Indigenous jurisdiction in the climate emergency

2025· article· W7119042633 on OpenAlexaffabout
Jocelyn Stacey, Emma Feltes, Russell Myers Ross

Bibliographic record

VenueJournal of Human Rights and the Environment · 2025
Typearticle
Language
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsIndigenousJurisdictionClimate changeColonialismStewardship (theology)Resistance (ecology)Vulnerability (computing)Climate justice

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0170.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.272
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

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