Wildfire risk awareness and prevention by predominantly Māori rural residents, Karikari Peninsula, Aotearoa New Zealand
Bibliographic record
Abstract
Worldwide, people use fire for a variety of purposes. Although researchers have learned how fire is used in many parts of the globe, relatively little is known about wildfire risk awareness and prevention activities by fire users. This paper presents results of a qualitative study in the Far North, Aotearoa New Zealand, where fire is used by residents primarily for burning vegetation on rural properties and household rubbish. Semistructured interviews and a focus group were completed with 25 predominantly Indigenous Māori residents to examine residents’ wildfire risk awareness, fire use and wildfire prevention. Participants’ high level of awareness of the local wildfire risk was due to their understanding of the local environment, past wildfires, attachments to land, information passed down within Māori whānau (extended families), and the local rural fire force. Awareness of the local wildfire risk, attachments to land, and efforts by the local fire force and residents encouraged participants to use fire safely, and abide by and carry out wildfire prevention initiatives. However, there was evidence of fire use contravening fire prevention regulations, including burning during restricted seasons without a permit and in prohibited seasons. Recommendations are provided to encourage safe fire use in Northland and beyond.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".