Stakeholder Consensus on Conservation Priorities Across Scientific, NGO, and Governmental Sectors
Bibliographic record
Abstract
ABSTRACT Understanding stakeholder perception is crucial for developing effective conservation strategies. Nevertheless, it is usually unclear which aspects are favored by different actors involved in environmental management. Here, we surveyed 354 stakeholders from 22 countries across the Mediterranean Basin to identify areas of agreement in their preferences. Despite broad variation in individual choices, we found a general consensus emerging across stakeholder groups (scientists, nongovernmental, and governmental organizations) on preferred ecosystem services, biodiversity facets, protected areas characteristics, and their relative importance. Specifically, our model identifies regulating ecosystem services, taxonomic diversity, and intrinsic value of nature as priorities for stakeholders. Conversely, the preferred characteristics of protected areas (e.g., size and accessibility) vary mostly based on individual preferences. We suggest that considering areas of stakeholder agreement when discussing management actions in the Mediterranean Basin will facilitate the adoption of area‐based conservation actions expected by the Kunming‐Montreal Global Biodiversity Framework. In the Mediterranean Basin, therefore, policymakers should strive to protect areas with high regulating ecosystem services, use taxonomic diversity to engage stakeholders, prioritize ecological targets to different characteristics of protected areas, and maintain the focus of area‐based conservation on nature itself. Implementing these action points should enhance support for conservation action in the region.
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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 | 0.031 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".