Stakeholder Perceptions Can Distinguish ‘Paper Parks’ from Marine Protected Areas
Bibliographic record
Abstract
While numerous Marine Protected Areas (MPA) have been created in the last decades, their effectiveness must be assessed in the context of the country’s biodiversity conservation policies and must be verified by local observations. Currently, the observations of local stakeholders, such as those from non-governmental organizations (NGOs), academics, government civil servants, journalists, and fishers, are not considered in any MPA database. The Sea Around Us has added observations from local stakeholders to address this gap, adding their perspectives to its reconstructed fisheries catch database, and to at least one MPA in each country’s Exclusive Economic Zone. It is important to pursue and incentivize stakeholder knowledge sharing to achieve a better understanding of the current level of marine protection, as this information is a valuable addition to the existing MPA databases. To address this gap, we demonstrated that personal emails containing a one-question questionnaire about the fishing levels in an MPA are an excellent way to gather data from local stakeholders, and that this works especially well for respondents in NGOs, academia, and governments. Of the stakeholders who replied to our personalized email, 66% provided us with the fishing level of the MPA that we asked for. The paper also presents how to access this information through the Sea Around Us website, which details in anonymized form the most common fishing levels for each selected MPA, as perceived or observed by different local stakeholder groups. This information is a unique and novel addition to a website that is concerned with marine conservation and contributes to a more accurate and inclusive discourse around MPAs. This information also helps to identify the gaps that need to be addressed to turn ‘paper parks’ (i.e., MPAs that are legally designated but not effective) into effective MPAs, which can contribute to climate-resilient ‘blue economies’.
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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 | 0.023 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".