Exploring perceptions to improve the outcomes of a marine protected area
Bibliographic record
Abstract
Marine protected areas (MPAs) are widely promoted as effective tools for conserving biodiversity and safeguarding ecosystem services. However, MPA success can be hindered by a lack of legitimacy and low social support because of perceived negative effects on ecosystem services and human well-being. Despite these social challenges, the social dimensions of marine conservation, such as the effects of MPAs on coastal livelihoods and local communities’ perceptions of these effects, are often overlooked in conservation initiatives. In this study, we use a mixed methods approach, combining qualitative thematic analysis and quantitative network analysis derived from interviews and focus group discussions, to examine the perceptions of key stakeholder groups about the Litoral Norte MPA in Portugal. Our findings reveal that most stakeholder groups hold more negative than positive views about the governance and management of the MPA. Key concerns include unsatisfactory participation in MPA decisions and perceptions that the MPA fails to deliver positive social and ecological outcomes, such as increased community involvement, fair income distribution, and enhanced fish abundance. Policy makers and managers need to address these negative perceptions to improve conservation governance and management. By considering the stakeholder feedback presented in this study, such as fostering better engagement with the local community and transforming conflicts into opportunities for co-developing new conservation actions with local resource users, policy makers and managers can increase support for Litoral Norte and enhance the social and ecological outcomes of the MPA.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| 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".