Improving Policy Coherence for Portugal’s Ocean Economy
Bibliographic record
Abstract
As one of the European Union’s largest coastal states, Portugal can make a significant contribution to sustainable ocean governance and achieving Sustainable Development Goal 14 Life Below Water. This report outlines a vision for aligning Portugal’s ocean economy with its global and regional commitments, focusing on the role of the Directorate-General for Natural Resources, Safety and Maritime Services (DGRM). This report examines how Portugal can strengthen policy coherence, governance, and resource allocation to foster a resilient blue economy. It highlights challenges and opportunities facing the DGRM and proposes actionable solutions for more integrated policymaking across maritime sectors. Grounded in the OECD’s Policy Coherence for Sustainable Development Recommendation, the report advises investing in workforce development, including capacities to identify policy interactions; enhancing digital infrastructure; simplifying administrative processes; and adopting a more agile agency model. These steps are critical for unlocking the synergies among sustainable blue growth, decarbonisation, and ocean digitisation. Supported by inclusive stakeholder engagement and robust evidence, this report offers a roadmap for transforming Portugal’s maritime sector.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".