The Politics of Pacific Ocean Conservation: Lessons from the Pitcairn Islands Marine Reserve
Bibliographic record
Abstract
Drawing on seventy-four interviews, this article analyzes the rising importance since the mid-2000s of large marine protected areas (MPAs) as a policy for managing ocean conservation. Governments have initiated eighteen large MPAs (over 200,000 km2) since 2006, reflecting the emergence of a new large MPA norm in marine conservation. This norm, we argue, emerged because of the success of a few transnational nongovernmental organizations (NGOs) in identifying politically feasible large MPAs, and then forming ad hoc domestic coalitions to lobby for them. This explanation is in contrast to most of the literature on how and why norms diffuse internationally, as well as existing explanations for the rise of large MPAs, both of which emphasize the importance of cohesive coalitions of transnational NGOs lobbying in multilateral venues. This bottom-up, international norm diffusion strategy has made large MPAs a viable policy option, one national jurisdiction at a time. For instance, this strategy was a critical element in convincing the UK to create the Pitcairn Islands Marine Reserve (835,000 km2) in 2015. Given the politics underlying the formation of large MPAs, where political gains have been high, and corporate and societal resistance relatively low, the creation of more large MPAs would seem likely, as occurred in 2016 when the UK announced it would designate three more large MPAs by 2020, totalling over 1.4 million km2. Growing support for large MPAs as a conservation strategy could also embolden states to establish large MPAs in more politically and economically contested waters, including on the Pacific high seas.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| 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".