Revaluations of 'Paiute Forestry': Prescribed Burning as Traditional and Scientific Ecological Knowledge
Bibliographic record
Abstract
The relationship between traditional and scientific ecological knowledge is a dynamic one. Consider the use of fire in land management. In the 1910s and 1920s, Aldo Leopold and other US foresters dismissively campaigned against burning as 'Paiute forestry', denigrating and driving out indigenous land management as though it had never existed, as though there was no credible ecological knowledge proir to settlement. Fire suppression as a longstanding policy across the US and Canada not only failed in reading historical tribal burning practices and in applying that knowledge to settler resource management, but also systematically undercut tribal ecological knowledge. A century later, scientists and restorationists are coming to better understand the dangers of fire suppression, benefits of burns, and the fact that these are not really new insights but epistemically marginalized ones. If prescribed burning is a complex assemblage of epistemic practices, and settler-colonial reactions have perpetrated epistemic injustices against indigenous peoples, how can modern (tribal, settler, and collaborative) burning practices and policies be better? In this project, I offer a close reading of early 20th Century light-burning debates, with a particular focus on Leopold’s characterizations of indigenous ecological knowledge. I then turn to critically evaluate several 21st Century prescribed burn projects and policies for their reparative potential in both social-ecological and social-epistemic terms.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.108 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| 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".