Marine protected areas governance, social norms, and social networks
Bibliographic record
Abstract
Establishing marine protected areas (MPAs) in places where communities live and work requires addressing the social implications of conservation. Social norms and social networks are bottom-up processes through which resource users self-organize to solve collective action problems. When formal institutions (e.g., regulations) build on these processes they are more likely to be effective and equitable. This research elicits social norms and maps an information-sharing social network (n = 81) in three small-scale fishing communities located near the Asinara MPA in Sardinia, Italy. Findings indicate that these communities are not grouped under one social network, but rather are split into three subgroups, which may at first suggest limited capacity for collective action given the apparent lack of social cohesion. Nevertheless, analyses on the distribution and strength of social norms denote that cooperative behaviours are still strong within the Asinara communities. Importantly, the second part of the analyses revealed the presence of central resource users, within each subgroup, who could act as bridges between the heterogeneous knowledge systems, ideas, and practices that have developed separately in each subgroup. Leveraging this diversity can generate new solutions to common challenges and further enhance collective action capacity for the whole Asinara social network. Understanding the social dimension of MPAs can enhance both the effectiveness and equity of governance and management arrangements (e.g., Target 3 of the Kunming-Montreal Global Diversity Framework) by highlighting areas where collective action is strong, enabling regulations to build on these existing processes, and by developing strategies to provide additional support where collective action is lacking. • Social norms and social networks help better understand fisher behaviour in the commons. • Knowledge on the social dimensions of MPAs can inform governance and management. • Communities can maintain strong norms of cooperation despite network fragmentation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".