Breathing Fire into Landscapes that Burn: Wildfire Management in a Time of Alterlife
Bibliographic record
Abstract
Across the globe, settler nation-states are being forced to contend with the large-scale ecological and social disruptions caused by settler colonialism. Wildfires are a charismatic example of this: when anthropogenic climate change combines with colonial forest management practices, wildfires act in ever changing ways with often violent and uneven impacts to human and nonhuman life. In a context of environmental change, managers, fire ecologists, and politicians alike are increasingly looking to reintroduce fire as a way of restoring “natural” forest landscapes while reducing fire suppression costs. In this paper, I examine one such policy of fire re-integration, in what is currently the Canadian province of Saskatchewan, the homelands of more than 50,000 Indigenous people (Cree, Dakota, Dene, Métis) who live in the province’s Boreal Forest region. In 2004, the Province implemented a controversial policy that locals colloquially refer to as “Let-it-Burn,” where fires are allowed to burn until they encroach upon something designated of “value” (typically human life, community structures, public infrastructure, and commercial timber). While wildfire managers, scientists, and politicians alike consistently advocate for policies of fire-reintegration as ecologically-sound and financially responsible ways forward with fire management, many locals have argued that “Let-it-Burn” is a direct affront to Indigenous sovereignty, destroying contemporary forest landscapes and rebuilding them through state-sanctioned settler values. Breathing fire back into landscapes that burn is a peculiar solution that at once acknowledges and erases the effects of fire’s removal through policies of restoration that risk ignoring the ongoingness of life in forested areas. Through interviews and archival and ethnographic fieldwork, this paper traces the history of the province’s “Let-it-Burn” policy, asking the question, “how to burn well in compromised lands?” As a way forward with fire reintegration (or not), I highlight the necessity of Indigenous partnership, leadership, and direction within fire management practices on Indigenous territory, which may include fire suppression. This paper adds to STS scholarship on ecological ruination and alterlife, arguing that wildfire management practices are likely to cause harm so long as the effects of settler colonialism are placed in the past and Indigenous rebuilding is erased.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".