Building the regional perspective: platforms for success
Bibliographic record
Abstract
ABSTRACT Regional approaches to protecting the marine environment have gathered momentum over the past 40 years. Pioneered by UNEP's Regional Seas Programme, such approaches have broadened their remit from pollution prevention to the conservation of biodiversity, promoting management tools such as networks of marine protected areas (MPAs). Formal intergovernmental approaches are increasingly complemented by a range of regional projects committed to ambitious targets to establish MPAs and Local Marine Managed Areas (LMMAs). These regional efforts have been inspired by political leaders, non‐governmental organizations, coastal communities and committed individuals. Regional networks of MPA managers have drawn together professionals to share good practice and further develop management tools. They focus on partnerships and capacity building opportunities with support from international donors and implementing agencies. Collective ecosystem‐based management delivered using a regional approach is identified as a preferred solution to environmental challenges in polar regions. Crossing boundaries and fostering regional synergies can help ensure ecologically coherent regional networks and support resilience. There is also the potential to reap tangible rewards from applying such a regional approach in many other areas. Regional coherence of MPA network design, compliance and enforcement policies, and information sharing is an optimal way to understand and counter commercial and industrial resource extraction forces actively working against sustainable development. 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 | 0.022 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".