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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".