Governance planning for sustainable oceans in a small island state
Bibliographic record
Abstract
Achieving the Sustainable Development Goals (SDGs) will require coordinated policymaking for achievement. Aruba is a Small Island State (SIDS) with 90% of its jobs and GDP dependent on the oceans has prioritized SDG 14 – life below water, or the SDG Ocean goal – for achievement. We have developed a planning process, building off of the the literature on SDG interactions and stratetic policy planning literatures, to guide SDG policy development and implemented it in Aruba. We used a structured expert elicitation process to carry out the analysis for this process. The process involves first identifying priority areas based on determining which SDG Ocean target provides the most co-benefit across other SDGs. Next we determine the SDG areas that most contribute to key SDG Ocean targets. Using this information we determine the key policy areas important for promoting sustainable oceans. Finally, we determine the Aruban ministries and institutions responsible for the various SDG areas and based on which SDG areas are most important for SDG Ocean achievement we visualize a new institutional network to support the achievement of SDG Oceans. First, we determined that while increasing economic benfits for SIDS (SDG 14.7) was the most important SDG Ocean target when considering direct impacts, reducing marine pollution (SDG 14.1), restoring marine habitats (SDG 14.2), and marine protection (SDG 14.5) were the most important SDG Ocean targets when considering indirect impacts. SDG areas with the most beneficial consequences for the SDG Ocean targets were mitigating climate impacts (SDG 13), international partnerships (SDG 17), jobs and economy (SDG 8), conserving terrestrial area (SDG 15), strengthening institutions (SDG 16), and promoting sustainable consumption and production practices (SDG 12). When links between SDGs are not considered, the institutional network supporting sustainable oceans is relatively simple, with the Department of Nature and the Environment most central: it coordinates across the largest number of relevant institutions supporting the SDG Oceans goal. However, when SDG relationships are considered, the institutional network is relatively complex, and the Social and Economic Council is determined to be the most central and important in coordinating activities across the largest number of Aruban instutions that support the SDG Ocean goal. Transitioning to a sustainable future requires policymaking that works across social-ecological dimensions, and need to design coherent and integrative institutional structures with which to do this.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".