Visualizing the landscape of blue finance for sustainable development: A bibliometric analysis and future directions
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
Abstract Blue finance research has recently made significant progress, but comprehensive research is still in its infancy. This paper established a consolidated database of 223 articles on blue finance and used CiteSpace for visualization analysis. Firstly, blue finance develops in embryonic, fluctuating, and stable growth phases. The main research countries are the US, Australia, England, and Canada. The University of California and the University of Queensland are the main research institutions. Marine Policy and Science are highly cited journals. A few core authors shape blue finance research with limited collaboration. Secondly, three themes were established by categorizing ten keyword clusters: financial instruments and mechanisms for marine conservation and sustainability, policy frameworks for adaptation and climate resilience, and policy frameworks for adaptation and climate resilience. Thirdly, contingent valuation, marine protected areas, and the blue economy are the main research hotspots. The results' theoretical contribution is identifying the progress of blue finance and its potential directions. Researchers, managers, and policymakers can use it to promote economic growth and ocean sustainability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.059 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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