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Record W4401193137 · doi:10.18280/ijei.070201

Identifying and Addressing the Right to Burn for Indigenous-Led Fire Stewardship Practices

2024· article· en· W4401193137 on OpenAlexaboutno aff
Trisia Megawati Kusuma Dewi, Herdis Herdiansyah, Tri Edhi Budhi Soesilo, Antar Venus

Bibliographic record

VenueInternational Journal of Environmental Impacts · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousStewardship (theology)Traditional knowledgeJurisdictionEnvironmental planningLegislationEnvironmental resource managementBiodiversityPolitical scienceBusinessGeographyEcologyPoliticsLaw

Abstract

fetched live from OpenAlex

In Canada, Indigenous peoples have been managing fires for generations.Challenges and alternatives related to power, jurisdiction, legislation, accreditation, liabilities, and resources exist in identifying and protecting forests from wildfires.Cultural burning can benefit community welfare, biodiversity, and wildfire risk reduction.This study compares Indigenous fire stewardship (IFS) in Canada with cultural burning practices in Indonesia, using literature and comparative research methodologies.Both countries face challenges to this issue.Canada allows cultural burning on reserves with supervision, while Indonesia permits local communities to burn up to two hectares without supervision.Community empowerment, Indigenous Ecological Knowledge (IEK), and fire management are crucial in both nations.Comparative analysis informs future cultural burning policies, emphasizing local expertise in risk reduction.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score0.686

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.005
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.029
GPT teacher head0.329
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2024
Admission routes1
Has abstractyes

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