Toward a Social Science Research Agenda for Large Marine Protected Areas
Bibliographic record
Abstract
Abstract Large marine protected areas (LMPAs) are a high‐profile trend in global marine conservation. Although the social sciences have become well integrated into marine protected area research and practice, human dimensions considerations have not been an early priority in the development of many LMPAs. This article argues that because LMPAs exhibit unique characteristics in form, function, and/or conceptualization, they warrant a distinct social science research agenda. We outline an agenda for social science research on and for LMPAs in four related themes: scoping of human dimensions, governance, politics, and social and economic outcomes. The article is informed by interviews, participant observation at the 2014 World Parks Congress, a literature review and the authors’ research experiences. LMPAs are at an early stage in what promises to be a globally significant, long‐term project of ocean conservation and governance. There is a timely opportunity to translate relevant insights from decades of social science research to LMPAs and generate new knowledge, where necessary, to give them their best chance at biological and social success.
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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 | 0.149 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.017 | 0.055 |
| Scholarly communication | 0.034 | 0.038 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.018 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".