Policy interactions in large‐scale marine protected areas
Bibliographic record
Abstract
Abstract Large‐scale marine protected areas (LSMPAs) have proliferated in recent years, now accounting for most of the world's MPA coverage. However, little is known about LSMPA outcomes and the factors that affect them. Here we argue that policy interactions—the cumulative effect of co‐existing policies for an issue and/or geographical area—can play a critical, but under‐recognized, role in influencing LSMPA design and outcomes. We analyze interactions between national LSMPAs within Palau and Kiribati, and regional fisheries management established by the Nauru Agreement to show how policy actors can account for policy interactions in LSMPA design, and to demonstrate the profound influence that policy interactions can have on the economic and conservation outcomes of LSMPAS. We draw on our analysis to distill lessons for our case studies and LSMPAs globally. We emphasize that policy interactions are dynamic and malleable: they should be proactively managed to stimulate synergy and address conflict. Understanding and managing policy interactions is complex and context‐specific, requiring dedicated resources, cross‐sectoral coordination, and sophisticated scientific and practical policy expertise. To avoid undesirable consequences and capitalize on opportunities to secure multiple benefits, we recommend that policy actors systematically evaluate, monitor, and adapt to policy interactions throughout LSMPA design and implementation.
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 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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".