Assessing policy coherence for developing a blue economy: a case study in the Republic of Panama
Bibliographic record
Abstract
The blue economy approach to ocean governance promises environmentally sustainable, economically viable, and socially equitable ocean-based economic growth. However, the blue economy has been inconsistently defined, interpreted, and applied, often leading to incompatibilities between the blue economy approach and existing ocean policies. We explore the blue economy in the Republic of Panama, where recent government commitments include designing and implementing a blue economy approach to ocean sector development. We use qualitative text analysis and a policy coherence assessment to examine the consistency of objectives across existing ocean policies in Panama and their compatibility with broader blue economy goals. Our results indicate that Panama’s existing ocean policies address some blue economy goals but also reveal how policy coherence assessments and precise deliberation can inform a more contextually sensitive blue economy approach that aligns with existing ocean policies while also adding value to ocean governance and better integrating blue economy objectives. Findings suggest that Panama’s existing ocean policies could better address social, environmental, and resource use objectives, without disregarding the need to reinforce economic and governance goals; elevating social objectives, especially social equity, can truly differentiate Panama’s blue economy from its current ocean governance approach. Finally, while we acknowledge that greater policy coherence can potentially increase the likelihood of attaining policy objectives, our findings show that coherence alone does not ensure their realization in practice. Our study contributes to blue economy scholarship by providing the first Latin America-based case study using policy coherence to assess compatibilities between existing ocean policies and a blue economy. Other countries seeking to transition to a blue economy could use our findings to inform the design of their approach and its integration with their existing ocean policy frameworks.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".