Governing the Land-Sea Interface to Achieve Sustainable Coastal Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.025 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it