Connecting wildfire risk reduction and land stewardship: social learning through adaptation pathways in Montseny, Catalonia (Spain)
Bibliographic record
Abstract
Wildfire management is becoming an increasingly complex issue that requires collaboration of diverse sectors. In addition, it is important to empower local communities to contribute to the decision-making processes. Social learning (individuals changing their understanding of an issue and furthering relationships) is a key ingredient in transdisciplinary collaborations aiming at complex social-ecological systems change. However, few studies in wildfire management consider social learning outcomes. This paper addresses this gap through a transdisciplinary case study: we explore how an adaptation pathways process has supported social learning outcomes for reducing wildfire risk in the Montseny and Tordera River watershed in Catalonia, Spain. We assessed how an adaptation pathways approach facilitated social learning outcomes (systems thinking, shared understanding, relational aspects, and substantive outcomes), and how this can benefit wildfire risk reduction efforts. Our findings show that the adaptation pathways process encouraged complex systems thinking among participants while addressing power relations in the territory, and provided creative ways to consider feasible local actions beyond administrative changes. We also observed how increased informal networks among participants play a role in achieving deeper social cohesion and land stewardship goals beyond wildfire risk reduction. Additionally, our social learning outcomes form part of longer-term processes of boundary-spanning work by local entities. Finally, the adaptation pathways provided an opportunity for innovative local wildfire governance that can be replicated in other areas of the world seeking more polycentric and anticipatory approaches that embrace complexity and encourage cross-sector synergies.
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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 | 0.003 | 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 | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".