Governmentality in Management of Coastal and Border Areas Based on Blue Economy in Riau Province
Bibliographic record
Abstract
This research aims to analyze the role of the concept of governmentality in the blue economybased governance of coastal and border areas in Riau Province to support economic sustainability and conservation of coastal resources.This research uses a qualitative method with an exploratory approach.Data collection techniques involve interviews with key informants and documentation, which is then analyzed using NVivo 12 Plus software to identify patterns of findings.This study shows that clear government visibility, adequate technical aspects, a rational basis for policy, and the formation of community identity are key factors in the blue economy-based management of coastal and border areas in Riau Province.Clarity of the government's role in managing coastal areas, including transparency in planning and implementing policies, increasing coordination between institutions, and strengthening community participation in maintaining the sustainability of coastal ecosystems.Adequate technical aspects, such as preparing sustainable Regional Spatial Planning (RTRW) and applying data-based technology, enable effective monitoring and evaluation of conservation programs.The rational basis for policies that prioritize the social welfare of coastal communities, the sustainability of the blue economy, and environmental conservation provides clear direction in the efficient management of natural resources.In addition, identity formation that involves education and digital literacy for coastal communities strengthens their capacity to adapt to change and supports the success of blue economy-based policies.Implementation of policies involving several sectors, such as fisheries, tourism, and industry, is also expected to reduce potential conflicts over space use, ensure more sustainable management, and encourage inclusive economic development in coastal and border areas of Riau Province.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".