The Global Norm of Large Marine Protected Areas: Explaining variable adoption and implementation
Bibliographic record
Abstract
Abstract Since 2006, governments have designated or announced 18 marine protected areas (MPAs) larger than 200 000 km2. Before then there was only one: Australia's Great Barrier Reef Marine Park, established in 1975. To explain this marked shift in state governance of marine biodiversity, this article points to the importance of a gradual strengthening over the past decade of a global norm that large MPAs, especially no‐take reserves, are valuable for meeting conservation objectives and targets. As is true for most global environmental norms, the large MPA norm emerged primarily out of civil society, especially from groups framing large MPAs as an effective way to help stop ocean decline. Importantly, however, the article demonstrates that the adoption of this norm is uneven across states, and implementation of large MPAs varies widely as governmental and non‐governmental forces interact – sometimes clashing, sometimes cooperating – with fishing, tourism and resource industries. For evidence, this article draws on fieldwork and 74 interviews across five large MPA cases: Papahānaumokouākea (2006) and the Pacific Remote Islands in the US (2009); the Coral Sea in Australia (2012); the Palau National Marine Sanctuary (2015); and the UK's Pitcairn reserve (2015). A comparative analysis of these cases reveals the influence of non‐governmental groups (especially The Pew Charitable Trusts and the National Geographic Society) on the gradual strengthening of the large MPA norm; the importance of the large MPA norm for the formation of marine policy; and the significance of domestic political economies for shaping variable norm adoption and state implementation. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment
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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 | 0.020 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".