Perception from a Public Survey of the Social–Ecological Effects of Wildfires in the Chiquitania Region of Bolivia
Bibliographic record
Abstract
In recent years, large-scale wildfires have become a serious threat to terrestrial ecosystems and people in the Chiquitania region of Bolivia. Understanding public perceptions is fundamental to designing comprehensive and effective wildfire management strategies. The objectives of the study were to learn perception on the main causes of wildfires, to understand their perceptions of the impacts of these events, and to explore the most viable solutions to preventing future wildfires in the Chiquitania region of Bolivia. We developed a 15-questions online survey and disseminated it through social media platforms, mobile messaging service groups, and at two workshops held in two locations. A total of 597 people participated in the survey with a balanced sex distribution. The participants were mainly young people aged 18–24 (45.40%) and 25–34 (21.40%), representing university students (42.6%) and professionals (42.6%). The data came from seven departments, but Santa Cruz was more strongly represented (75.9%). In addition, although only 65% considered themselves part of the general population, the data shows that 76% had personal experience of wildfires. Respondents indicated that fires were caused by human activities (95.9%), mainly due to traditional agricultural practices. The most important perceived impacts included landscape and vegetation quality, fauna habitat and ecosystem regeneration. In addition, participants have prioritized the reinforcement of patrols and surveillance, the hiring of forest firefighters and the purchase of aerial firefighting units. For prevention, the most chosen was to change policies that promote fires, changing the vision for economic development and stricter penalties. The findings can be used to formulate public policies aimed at preventing wildfires, mitigating their impacts and promoting environmental conservation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".