Peran regulasi Pemerintah Daerah dalam optimalisasi belanja daerah untuk meningkatkan daya saing sektor unggulan Kabupaten Gunungkidul
Bibliographic record
Abstract
Purpose – This study aims to analyze the competitiveness enhancement of key sectors in Gunungkidul Regency during the 2014–2023 period and to formulate strategic recommendations for future policy improvement.Methods – The research employs a shift-share analysis and Pearson correlation test to examine the relationship between development expenditure, private investment, and regional economic growth.Findings – The findings indicate that development expenditure has a positive correlation with economic growth. However, its effectiveness largely depends on appropriate allocation policies and the quality of budget implementation.Implication – Local governments need to strengthen strategies for optimizing development expenditure, focusing on infrastructure development, workforce training, and technological innovation to enhance the competitiveness of key sectors.Originality – This study contributes to the enhancement of sectoral competitiveness in Gunungkidul Regency by applying a combination of shift-share analysis and Pearson correlation methods. AbstrakTujuan – Penelitian ini bertujuan untuk menganalisis peningkatan daya saing sektor unggulan di Kabupaten Gunungkidul selama periode 2014–2023, serta merumuskan rekomendasi strategis untuk perbaikan kebijakan di masa mendatang. Metode – Penelitian ini menggunakan metode analisis shift-share dan uji korelasi Pearson untuk mengkaji hubungan antara belanja pembangunan, investasi swasta, dan pertumbuhan ekonomi daerah. Temuan – Hasil penelitian menunjukkan bahwa belanja pembangunan memiliki korelasi positif terhadap pertumbuhan ekonomi. Namun, efektivitasnya sangat bergantung pada kebijakan alokasi serta implementasi anggaran yang tepat dan terarah. Implikasi – Pemerintah daerah perlu memperkuat strategi optimalisasi belanja pembangunan dengan menitikberatkan pada peningkatan infrastruktur, pelatihan tenaga kerja, serta pengembangan inovasi teknologi guna mendorong peningkatan daya saing sektor unggulan. Orisinalitas – Penelitian ini memberikan kontribusi dalam upaya peningkatan daya saing sektor unggulan di Kabupaten Gunungkidul melalui penerapan metode shift-share dan uji korelasi Pearson secara simultan.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".