Introduction: an empirical framework for deconstructing the realities of governing marine protected areas
Bibliographic record
Abstract
Debates surrounding governance strategies for marine protected areas (MPAs) have to date largely focused on top-down, bottom-up or market-based approaches. Whilst co-management approaches for governing MPAs are widely accepted as a way forward for combining these three strategies, many interpretations of this concept exist and it is applied in many different ways in MPAs in different contexts. This study aimed to explore governance through a case-study approach based on a specifically developed empirical framework – the marine protected area governance (MPAG) analysis framework – to increase understanding of how to combine the three governance approaches. A dialogue with MPA practitioners in 20 case studies helped shape the MPAG analysis framework as it developed, and an international workshop was held on ‘Governing MPAs’, bringing the practitioners together to compare results and further develop the framework. This paper provides an overview of the topic and research methodology and briefly introduces the case studies further explored in this special issue
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".