Participatory mapping reveals socioeconomic drivers of forest fires in protected areas of the post‐conflict Colombian Amazon
Bibliographic record
Abstract
Abstract Wildfires have increased in protected areas (PAs) of the Colombian Amazon following the 2016 peace agreement between the Government and the Revolutionary Armed Forced of Colombia (FARC—Spanish acronym). Recent study efforts to understand this issue suffer from data scarcity and limited consultation of local stakeholder perspectives on factors affecting wildfires. This study uses a social–ecological systems framework to investigate local perceptions of factors driving and/or preventing wildfires in the Los Picachos, La Macarena and Tinigua PAs, which are shared by two Amazonian departments experiencing wildfire increase. Four stakeholder categories were selected to represent varied and possibly conflicting interests: cattle ranchers, the national park service, local authorities and cross‐sectional stakeholders. We combined a participative mapping approach with interviews to illustrate stakeholder perceptions of interactions between key variables in graphical causal models. Network analyses were used to determine areas of agreement on key variables, and to compare local priorities with those of key informants at the national level. Local stakeholders and key informants widely agreed on the roles of extensive cattle ranching and land grabbing as key drivers of wildfires. The analysis identified areas for further research into wildfire occurrence within PAs. These include lack of governance and untitled land, as well as the effects of poor access to basic public services on unsustainable ranching methods. This study revealed contested opinions between ranchers and other stakeholders over interactions between ranching, roads and illicit crops, and consequently their effects on wildfire occurrence. This indicates the need for cautious implementation of the National Development Plan, prioritising road maintenance over expansion, integrating arable alternatives to cattle ranching and considering multiple stakeholders in regional decision‐making around wildfire reduction. The strengths and limitations of the participative mapping approach employed here are discussed with a view to aiding decision‐making in post‐conflict regions of the Global South.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".