Achieving Success under Pressure in the Conservation of Intensely Used Coastal Areas
Bibliographic record
Abstract
Understanding how biological conservation and socioeconomic development can be harmonized in socialecological systems is at the core of sustainability science.We present the case of a Mediterranean marine protected area (MPA), the Tavolara-Punta Coda Cavallo MPA, that exhibits high ecological performance under intense pressure from fishing, tourism, and coastal development.This case study illustrates how socioeconomic development and significant conservation benefits can coexist, even in a challenging context.Based on this case study, we present a framework for what elements and interactions have determined the high ecological performance of this MPA, and highlight the key leverages that have enabled ecosystem recovery.In particular, the most critical elements underlying high performance were sufficient leadership and knowledge to identify a conservation vision and to catalyze some key actors in the implementation of this vision.Thus, success was ultimately determined by the ability of the leadership of the MPA to devise and implement an effective strategy, with the support and participation of key actors that were external to the MPA organization.The insights from this case study may be applicable to improving MPA management in other systems with similar characteristics, including high human pressures and the presence of an MPA authority.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".