Stakeholder perspectives on large-scale marine protected areas
Bibliographic record
Abstract
Large-scale marine protected areas (LSMPAs), MPAs greater than 100,000km2, have proliferated in the past decade. However, the value of LSMPAs as conservation tools is debated, in both global scientific and policy venues as well as in particular sites. To add nuance and more diverse voices to this debate, this research examines the perspectives of stakeholders directly engaged with LSMPAs. We conducted a Q Method study with forty LSMPA stakeholders at five sites, including three established LSMPAs (the Marianas Trench Marine National Monument, United States; the Phoenix Islands Protected Area, Kiribati; the National Marine Sanctuary, Palau) and two sites where LSMPAs had been proposed at the time of research (Bermuda and Rapa Nui (Easter Island), Chile). The analysis reveals five distinct viewpoints of LSMPAs. These include three more optimistic views of LSMPAs we have named Enthusiast, Purist, and Relativist. It also depicts two more cautious views of LSMPAs, which we have named Critic and Skeptic. The findings demonstrate the multi-dimensionality of stakeholder viewpoints on LSMPAs. These shared viewpoints have implications for the global LSMPA debate and LSMPA decision-makers, including highlighting the need to focus on LSMPA consultation processes. Better understanding of these viewpoints, including stakeholder beliefs, perspectives, values and concerns, may help to facilitate more nuanced dialogue amongst LSMPA stakeholders and, in turn, promote better governance of LSMPAs.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".