“Forest doesn’t burn anymore”: Indigenous ontologies of cultural burning and fire in Southern India
Bibliographic record
Abstract
Historically, many Indigenous communities across the world have practiced seasonal burning to manage their local ecosystems. This practice shaped forest terrains worldwide and established an intentional and active relationship between humans and fire. Yet, perceived as a threat to wildlife and biodiversity, colonial policies have banned the burning of forests, and customary fire management remains a point of disagreement between conventional forest managers and Indigenous Peoples. This is particularly evident among Adivasis (Indigenous Peoples of India), whose use and understanding of fire are often disregarded in forest management, and the prohibition on traditional burning practices continues in India. Through open-ended interviews and transect walks in the Wayanad Wildlife Sanctuary in the South Indian state of Kerala, we discuss Adivasi engagements with cultural burning. In this study, Adivasi members characterized fire as: (1) a preserver and groomer of landscape identity; (2) a co-manager and actor within specific forest terrains; and (3) an enabler of socio-ecological function and relationships. Through this, we explain human–fire coexistence—what enables it and how it benefits human–forest systems and fire's multifaceted role in many ecosystems. Fire clears up forests and supports Adivasi mobility, access within and interactions with forest ecosystems, and coexistence with wild animals. It enables the growth of grass and native trees, leading to a productive, functional, and healthy “good forest.” We argue that alternative fire dialogs provide opportunities for land management policies that better reflect distinct fire ontologies and for the practices that might then follow. Additionally, as forest fires increase, revisiting Indigenous perspectives can offer lessons for coexistence with fire to minimize its consequences in future landscapes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".