A biocultural approach to navigating conservation trade-offs through participatory methods
Bibliographic record
Abstract
The desire to simultaneously address the well-being of local people while also mitigating the destruction of ecosystems resulted in a variety of win-win approaches, including popular models such as community-based conservation and integrated conservation and development projects. More than 25 years of international conservation experience show that win-win outcomes are decidedly mixed; there is a need to shift to a trade-off narrative to make these approaches more effective and sustainable. In this article we consider how a biocultural approach could provide relevant information to better understand and navigate trade-offs in protected area planning and management processes. Using these central tenets, this research uses participatory mapping methods to identify and document communities’ physical and cultural landscapes and how they are connected. We then utilize community visioning facilitation to create a shared vision of participatory forest management. The results indicate that this approach can identify geographic boundaries and spatial biocultural resource-use patterns, uncover those resources’ cultural relevance, and cultivate a foundation for more meaningful participation for communities in the protected area planning and management processes.
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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 | 0.080 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".