Leverage points and levers of inclusive conservation in protected areas
Bibliographic record
Abstract
Inclusive conservation approaches that effectively conserve biodiversity while improving human well-being are gaining traction in the face of the sixth mass extinction of biodiversity. Despite much theorization on the governance of inclusive conservation, empirical research on its practical implementation is urgently needed. Here, using a correlation network analysis and drawing on empirical results from 263 sites described on the web platform of the PANORAMA initiative (IUCN), we inductively identified global clusters of conservation outcomes in protected and conserved areas. These clusters represent five conservation foci or archetypes, namely (i) community-based conservation, (ii) sustainable management, (iii) conflict resolution, (iv) multi-level and co-governance, and (v) environmental protection and nature’s contribution to people. Our empirical approach further revealed that some dimensions of inclusive conservation are crucial as leverage points to manage protected areas related to these clusters successfully, namely improvements in the socio-cultural context and social cohesion, enhancing the status and participation of youth, women, and minorities, improved human health, empowerment of local communities, or reestablishment of dialogue and trust. We highlight inclusive interventions such as education and capacity building, development of alliances and partnerships, and enabling sustainable livelihoods, or governance arrangements led by Indigenous peoples and local communities or private actors, as levers to promote positive transformations in the social-ecological systems of protected areas. We argue that although some of the leverage points we identified are less targeted in current protected area management, they can represent powerful areas of intervention to enhance social and ecological outcomes in protected areas.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".