ANALISIS SEKTOR UNGGULAN UNTUK MEWUJUDKAN KOTA MAGELANG YANG MAJU DAN BERDAYA SAING
Bibliographic record
Abstract

 
 
 
 Mengidentifikasikan sektor unggulan menjadi penting untuk dilakukan terutama dalam menentukan strategi pembangunan ekonomi ke depannya. Oleh karenanya, penelitian ini dilakukan dalam rangka meningkatkan daya saing ekonomi Kota Magelang sebagaimana visi Kota Magelang yang tercantum dalam RPJPD tahun 2005-2025 yaitu “Magelang sebagai Kota Jasa yang Berbudaya, Maju dan Berdaya Saing dalam Masyarakat Madani”. Metode yang digunakan adalah analisis gabungan dari Location Quotient (LQ) statis dan dinamis, Shift-share, Model Rasio Pertumbuhan (MRP), Overlay dan Tipologi Klassen. Hasil penelitian menunjukkan terdapat 4 (empat) sektor yang masuk ke dalam sektor maju dan berkembang pesat; Pengadaan Listrik dan Gas; Transportasi dan Pergudangan; Administrasi Pemerintahan, Pertahanan, dan Jaminan Sosial Wajib; dan Jasa Pendidikan. Strategi pembangunan ekonomi untuk meningkatkan daya saing ekonomi Kota Magelang dapat dilakukan dalam jangka pendek, menengah, dan jangka panjang.
 
 
 
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads agree on what is shown here.
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".