Risk perceptions after wildfires: insights from Bijie, China and comparisons with other countries
Bibliographic record
Abstract
Background Wildfires are increasingly frequent and intense due to climate change and human activities. Public risk perceptions after wildfires play a critical role in wildfire management, but there is a lack of specific studies in China. Aims This study focused on Bijie, China, to analyze local perceptions and compare them with global cases. It investigated how factors such as information about fire situations and economic losses, trust in government and confidence in tackling wildfires influenced risk perceptions after wildfires, and explored how risk perceptions differed across cultural contexts in China and in other countries. Methods Using the ‘information-trust-confidence’ framework, a survey of 408 residents in Bijie was conducted. Principal component analysis (PCA) was used to assess the relationships between risk perceptions and factors. A comparative analysis with the United States (US), Australia, Canada, Europe and other regions was also performed. Results There is a negative correlation between risk perception and each of trust, information and confidence. Older people and males showed lower risk perceptions, while ethnic minorities have lower perceptions compared to the Han ethnic group in China. International research on risk perceptions after wildfires has highlighted diverse methodologies and yielded valuable insights, with comparisons revealing distinct differences across countries and regions.
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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".