How policy interventions influence burning to meet cultural and small-scale livelihood objectives
Bibliographic record
Abstract
Fire has cultural and economic significance for Indigenous and rural peoples worldwide, being used to manage landscapes for activities such as hunting, gathering, cropping, and forestry, and for ceremonial and spiritual purposes. Policy interventions by state and non-state organizations, such as regulations, economic incentives, and communication campaigns, can target fire use directly, or affect it indirectly, for example, by restricting land access. Yet evidence of such impacts has not been synthesized at the global scale. We analyzed 512 examples in 68 countries to describe the range of policy interventions by state and non-state organizations that target and/or affect fire use, categorizing interventions based on the broad actor types involved, their mode of operation (e.g., regulation) and their intentionality and/or possible effects vis-a-vis fire use. Of these interventions, 74% involved only state agencies in policy design and implementations, 4% involved only non-state organizations, and 18% involved collaboration between state and/or non-state organizations and/or communities. Three hundred and nine interventions directly targeted fire use, of which 87% aimed to eliminate or constrain fire use. Two hundred and three affected fire use indirectly, of which 88% led to reductions in or constraints upon fire use. Though there is some recognition in the 21st century of a need, in certain contexts, to support local fire use, for reasons related to environmental justice, ecology, wildfire risk and climate change, the literature we reviewed points to several challenges for contemporary efforts toward this end. These include contradictions between policy interventions, mistrust between actors following histories of fire suppression, greater fuel loads increasing the risk of burning where fire has been suppressed, and the need to consider the indirect effects of other types of policy, such as those related to land tenure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".