Protected areas under pressure: An online survey of protected area managers regarding social and environmental conservation target attainment and stakeholder conflicts
Bibliographic record
Abstract
The Sustainable Development Goals (SDGs) recognize the interconnectivity between a diverse set of agendas. Identifying policies and practices that can reconcile incompatibilities across goals remains a challenge. With concurrent biodiversity and climate crises, the world faces increased pressure to find effective options for building resilience in remaining intact landscapes and ecosystems, which is critical for making progress toward several SDGs. While pressures on biodiversity and ecosystems have intensified dramatically, recent studies have shown a high geographic coincidence between important ecosystems and social conflict. Considering this, it is critical to understand how to manage Protected Areas (PAs) to effectively deliver environmental and social dividends while also minimizing or effectively managing stakeholder conflict. In this study, we present the results of an online survey of PA managers administered in 2021 to explore these themes. We conduct correlational analysis to identify patterns in survey data provided by PA managers from a variety of geographies. Building upon previous studies on the interlinkages between PA governance and social conflict, our results highlight the real challenge conflict presents for conservation efforts. Our findings also indicate that broader and more inclusive involvement of a wide range stakeholders in PA management is critical for enabling better environmental and social outcomes, but many PA managers lack the resources and support needed to effectively involve a wide array of stakeholders. Future studies should undertake case studies and qualitative work to uncover the dynamics between stakeholder conflicts, exogenous shocks, and conservation in specific contexts.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".