Our Coastal Futures: pathways to sustainable development
Bibliographic record
Abstract
The world’s sustainability opportunities and national security challenges converge in the coastal zone. Hundreds of millions of people face increasing pressure from population growth, over exploitation of natural resources, and escalating disaster-risk, as the climate changes and sea levels rise. Global Environmental Assessments have been a tool for international policy makers, and a particular favorite of UN bodies and can add value to national efforts. The first World Ocean Assessment concluded that without an integrated, coordinated, proactive, cross-sectoral and science-based approach to coastal and marine management, the resilience of coastal and marine ecosystems, and their ability to provide vital services, will continue to be reduced. The second World Ocean Assessment is currently under development and will build on the baselines established in the first assessment, by identifying key trends and relevance to the SDGs. However, global assessment processes, may be curtailed by bureaucracy and diplomatic legitimacy, whilst struggling to engage relevant stakeholders and institutions. As a result, there is a need to complement these important top-down, global environmental assessments, with more agile assessment processes, as well as facilitating bottom-up capacity building with stakeholders involved in coastal zone management. This requirement is articulated in the text of the “Our Coastal Futures Strategy”, launched in 2018 by Future Earth Coasts (FEC). We, the FEC program, observe that the assessment process is often not as inclusive as it could be, for example, by separating sectors, such as science and technology, policy, and public engagement throughout the assessment, with inter-sector connections usually made at a later stage in the process. Therefore, we stress the importance of co-designed synthesis that can reach into new knowledge, including tacit knowledge of diverse stakeholders, from the outset. Our Rapid Ocean Assessment Methodology Workshop will address this important issue and achieve a better understanding of the complexities and non-linearities of coastal-zone processes and interactions; fundamental to informing meaningful assessments and identifying potential sustainability pathways.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.124 | 0.035 |
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".