Coral Reef Governance: Strengthening Community and Collaborative Approaches: A Vibrant Oceans Initiative Whitepaper
Bibliographic record
Abstract
Climate change, unsustainable fishing, and land-based pollution (Ainsworth et al. 2016, Cinner et al. 2018, Hughes et al. 2018, Wenger et al. 2020) are among the top pressures to coral reefs globally, resulting in substantial losses of live coral cover (Eddy et al. 2021) and the loss of ecosystem services valued at more than $10 trillion dollars per year (Costanza et al. 2014).Strengthening the enabling conditions for successful coral reef conservation is one of the most pressing challenges facing communities, scientists, managers, policymakers, non-governmental organizations (NGOs), and philanthropic donors in the 21st century, and will require significant investments to improve governance of coral reefs and the human activities that threaten them (Morrison et al. 2019).Successful local governance underpins two key aspirations of conservation success: better outcomes for biodiversity goals and ensuring that the needs and aspirations of local communities connected to coral reefs are met with sustainable, equitable, and just management.This whitepaper offers insights for improving coral reef governance, drawn from leading research on biodiversity conservation and environmental governance.The paper identifies a set of foundational principles for strong community-based coral reef governance grounded in the work of Elinor Ostrom and further lessons for building, strengthening and supporting community-based governance.These include support for local decision-making, building and linking social, institutional, natural, human and financial capital across scales, scaling-up conservation successes, diversifying approaches to conservation, supporting equity, rights, and justice, and monitoring and management of emerging threats.Although coral reef conservation and governance is place-based and context-specific, there remain several opportunities for stakeholders to contribute to conservation objectives by: Rebuilding and strengthening local institutions.Planning for long-term funding. Sharing diverse voices and experiences.Ensuring diverse knowledge for decision-making.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".