Socioeconomic-Based Management Strategies for Industrial Areas in West Sumbawa Regency
Bibliographic record
Abstract
West Sumbawa Regency possesses significant potential for the development of industrial areas, bolstered by its abundant natural resources and strategic location.However, suboptimal management of these areas has led to various socioeconomic challenges.This study aims to propose a comprehensive management strategy for industrial areas in West Sumbawa Regency that integrates socioeconomic considerations.A qualitative methodology was employed, with data collected through interviews, observation, and documentation.The data were analyzed using Nvivo 12 Plus software and the analysis of strengths, weaknesses, opportunities and threats (SWOT).The findings indicate that current industrial area management is insufficient, as evidenced by limited community involvement, low quality of life for residents near industrial zones, and underutilization of local resources.Key strategies proposed include the establishment of a dedicated institution for integrated and sustainable management of industrial areas, human resource development focused on enhancing the skills and knowledge of local communities, and economic empowerment through providing access for local communities to participate in industrial value chains and developing small and medium enterprises (SMEs).Furthermore, improving community well-being by enhancing access to education, healthcare, and basic infrastructure is essential.The adoption of sustainable environmental management practices, such as the implementation of eco-industrial park (EIP) principles, is recommended to minimize the environmental impact of industrial activities.The proposed socioeconomic-based management strategy is expected to enhance community welfare and mitigate the adverse effects of industrial area development in West Sumbawa Regency.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".