Conceptualizing Social Outcomes of Large Marine Protected Areas
Bibliographic record
Abstract
There has been an assumption that because many large marine protected areas (LMPAs) are designated in areas with relatively few direct uses, they therefore have few stakeholders and negligible social outcomes. This article challenges this assumption with diverse examples of social outcomes that are distinctive in LMPAs. We define social outcomes as inclusive of both social change processes and social impacts, where “social” includes all perceptual or material human dimensions. We draw on five in-depth case studies to report social outcomes resulting from proposed or designated LMPAs in Bermuda, Rapa Nui (Easter Island), Kiribati, Palau, and the Commonwealth of the Northern Mariana Islands & Guam. We conclude: (1) social outcomes arise even in remote LMPAs; (2) LMPA efforts generate social outcomes at all stages of development; (3) LMPAs have the potential to produce outcomes at a higher level of social organization, which can change the scope and type of affected populations and, in some cases, the nature and stakes of the outcomes themselves; (4) the potential for LMPAs to impart distinctive social outcomes results from their unique geographies and/or intersection with high-level politics and policy processes; and (5) social outcomes of LMPAs may emerge in the form of social change processes and/or social impacts.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| 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".