Integration of Blue Economy and Business Continuity Plan in Strengthening the Economic Resilience of Coastal Communities in Southern Java, Indonesia
Bibliographic record
Abstract
This study aims to analyze the role of the blue economy and government support in strengthening the economic resilience of coastal communities in southern Java through the mediation of a Business Continuity Plan (BCP).The research methodology uses a quantitative approach with surveys in four coastal districts, analyzed through a structural equation model.The results indicate that the blue economy does not directly influence economic resilience but significantly contributes through the BCP, with its effectiveness depending on community awareness and capacity to develop adaptive and sustainable business planning.Conversely, government support is proven to have both direct and indirect impacts, through policies, training, infrastructure, and technical assistance that strengthen the local economic foundation while promoting the implementation of the BCP.These findings emphasize that BCP serves as a strategic mechanism linking the blue economy and government support to enhance the economic resilience of coastal communities.This study provides theoretical implications for the development of coastal-based economic resilience literature, as well as practical implications for the formulation of sustainable development policies, emphasizing the integration of adaptive planning and government community collaboration to create a resilient coastal business ecosystem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".