Human Dimensions of Large Marine Protected Areas
Bibliographic record
Abstract
In 2014, a team of social scientists at Colorado State University, Duke University, and University of Guelph initiated an ambitious research and outreach project to generate and share new knowledge about the human dimensions of large MPAs. The project approaches large MPAs as a global movement, with an empirical focus on large MPAs proposed in Bermuda and Rapa Nui (Easter Island, Chile) and designated in Kiribati, Palau, and the Commonwealth of the Northern Mariana Islands & Guam. In some of our case studies, large MPAs have been designated for a number of years; in others, designation seems unlikely in the near future if at all. As our selection of cases illustrates, we believe there is much to learn about the human dimensions of large MPAs not only when they are implemented, but also during efforts to establish them. Our goal is to advance understanding of the emergence, form, and function of large MPAs as a governance tool and, ultimately, to inform decision-making and debates regarding large MPAs within case study sites and globally. This website is one way of sharing information about our project. We are also pleased to be working with other scholars and practitioners who are engaged in a growing ‘Community of Practice’ focused on the human dimensions of large MPAs, including those who co-organized and participated in a “Think Tank” in February 2016. Our project is part of a broader effort to understand the human dimensions of large MPAs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".