A Systematic Review: Mirror-Mirror on the Wall, What is the Relationship Between Blue Economy and Community Development?
Bibliographic record
Abstract
The purpose of this paper is to examine the relationship between the blue economy and community development toward improving living standards and livelihoods.A systematic literature review was conducted involving a total final sample of 15 articles published during 2017-2022.Specifically, the source of the database used in this study is Scopus, and Web of Science based on a set of inclusion/exclusion criteria for analysis and synthesis to meet the purpose of the paper.This study employed the thematic analysis method for the systematic literature review.The important components in this study are coastal resources, employment and society, and policy governance in the blue economy showing the innovations that have been successfully explored in this study.The blue economy is highly dependent on the cooperation of local communities in preserving the environmental treasures in developing sustainable development for the country.The community must have the courage to venture into the field of ocean activities because it is one of the main contributors to sustainable economic growth.In addition, the stakeholders who manage the governance of the oceans have a great impact on the change of a component to a new economic concept that can be a catalyst for economic growth.This paper seeks to contribute, analyze limited articles on the blue economy and community development toward improving living standards, and identify further research areas.
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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 | 0.110 | 0.366 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".