Upscaling marine and coastal restoration through legal and governance solutions: Lessons from global bright spots
Bibliographic record
Abstract
There is a global imperative to upscale restoration in line with the Kunming-Montreal Global Biodiversity Framework. Upscaling of marine and coastal restoration is hindered by legal and governance barriers. Identifying both the types of barriers and potential solutions from global ‘bright spots’ is a first step toward implementing legal and governance frameworks to facilitate upscaling of marine and coastal restoration. Here we identify five types of barriers including (a) lack of fit-for-purpose permitting frameworks, (b) tenure issues, (c) concerns regarding risk and liability, (d) a lack of overarching targets for restoration, and (e) uncoordinated governance frameworks. For each barrier, we conduct a broad analysis of legal and governance solutions from across the world. Our analysis provides a guide for future research and law and governance reform.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".