Social acceptance in coastal and marine protected areas: Port-cros national park (France)
Bibliographic record
Abstract
The effectiveness of marine protected areas is a timely issue, as the fight against biodiversity loss depends significantly on how effective these areas are. This article explores a key condition for meeting the challenge of improving conservation policies, as part of the global targets for biodiversity conservation, following the 2022 Kunming-Montreal Global Biodiversity Framework. The paper explores the determinants of social acceptance of Marine Protected Areas (MPAs) by local communities impacted directly by MPAs designation. We focus on the Port-Cros National Park (France), which was established in 1963. The park has seen its governance, perimeter and internal zoning evolve over time, generating local opposition and underlining the importance of acceptance by local communities. This article highlights the factors that play a role in the social acceptance of this National Park based on the results of a questionnaire survey including 569 residents of the municipalities in and around the protected area. The approach we followed is part of a European research project that has developed a methodological protocol to identify interactions between social parameters that characterise the level of support and has been applied in 20 national parks. Our results reveal a high level of social acceptance for the National Park, with four main factors influencing acceptance: perceptions of the social impacts of the park, trust in management authorities, sociodemographic characteristics and conditions for citizen participation. Spatial disparities are also demonstrated. Our analysis also enables us to explain the components of constrained acceptance: this opens possibilities to explore the conditions under which local communities can feel more empowered and participate in the governance of the protected area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".