Merging top‐down and bottom‐up approaches in marine protected areas planning: experiences from around the globe
Bibliographic record
Abstract
ABSTRACT Five case studies from around the world illustrate key lessons in integrating top‐down and bottom‐up approaches to stakeholder and community engagement in the planning and implementation of marine protected areas (MPAs). Community resistance to MPA proposals from centralized agencies can be addressed through effective participatory processes with consistent engagement over time, transparency, and the incorporation of benefits for communities. Indigenous communities in particular are becoming key actors of some conservation initiatives (e.g. MPAs) and recognition of their inherent rights, traditional knowledge and deep connections to the marine environment can become the foundations for collaborative management of MPAs. True participation requires empowerment for engagement, and this in turn requires education and capacity building for local people to get involved in the process of planning, implementing, and managing MPAs. How bottom‐up and top‐down approaches are used should consider the scale of the MPA (e.g. small vs. large), the geographic scenario (e.g. coastal vs. remote), the level of anthropogenic influence, the conservation objectives (e.g. species, habitats, ecosystems), the political and governance context, and specific cultural conditions, such as the presence of indigenous communities. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| 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".