Examining stakeholder involvement in the context of top-down marine protected area governance: The case of the Sept-Îles National Nature Reserve (Brittany, France)
Bibliographic record
Abstract
Marine protected areas (MPAs) are important yet complex conservation tools that can be difficult to govern and manage. In France, the State manages protected areas with national status, but consults communities and users when making decisions. How can the governance of an MPA be improved while respecting the framework imposed on it by State regulations? This study focusses on the Sept-Îles National Nature Reserve (Réserve naturelle nationale, or RNN), located in northern Brittany (France) and renowned for its natural heritage, particularly for its seabird conservation efforts. Its management methods are provided for by the French Environmental Code, and are structured around an Advisory Committee, a Scientific Council, and a designated manager. Any change in the functioning of this committee must comply with the provisions of French law. Following a decree to extend the perimeter of the RNN, there was the opportunity to reassess the functioning of the current governance structure the RNN Sept-Îles and to define its strengths and weaknesses so that these may be addressed as the RNN grows. Various stakeholders – for the most part members of the Advisory Committee – were engaged through semi-structured interviews, guided by the principles of good governance. This study found that the current structure of the Advisory Committee is not aligned with the French Environmental Code and proposes new working groups that could offer stakeholders more opportunities for participation. There were issues of representation, communication, and power struggles within the Advisory Committee and highlights a distinct lack of young people within the governance structure of the RNN, which poses questions about its future. This is one of the first studies in France to propose an alternative governance structure involving more RNN stakeholders that can fit into the current framework imposed by State regulations.
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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 | 0.013 | 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 | 0.016 | 0.014 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".