Governing the Land-Sea Interface to Achieve Sustainable Coastal Development
Bibliographic record
Abstract
Coastal regions are essential to achieving the Sustainable Development Goals (SDGs) given their importance for human habitation, resource provisioning, employment, and cultural practice. They are also regions where different ecological, disciplinary, and jurisdictional boundaries both overlap and are obscured. We thus propose the land-sea interface as areas where governance systems are most in need of frameworks for systems analysis to meet the SDGs—which are inherently interconnected— and integrate complex interdependencies between human livelihoods, energy, transport, food production, and nutrient flows (among others). We propose a strategic land-sea governance framework built on the sustainable transitions literature to plan for governance to achieve sustainable development across the land-sea interface. To illustrate our proposal, we compare governance planning processes across four case-based scenarios: an industrialized coastal country, a least developed coastal country, a developing coastal country with local dependencies on ocean resources, and a small island developing state primarily dependent on tourism. Through the lens of aligning governance actors and actions vertically (subnational to national), horizontally (across sectors), and programmatically (from goals to implementation), we propose scales at which governance systems may be misaligned, such as where different agencies that affect marine systems have conflicting visions and goals, leading to stalled progress or counterproductive actions. Where possible, we also highlight strategies to align across scales of high level strategic policy, tactical scale institutional mandates and cooperation, and on the ground activities and operations, such as aligning actors based on an analysis of interdependencies of goals.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".