ANALISIS SUMBER- SUMBER PERTUMBUHAN EKONOMI DAN KETIMPANGAN WILAYAH DI PROVINSI NUSA TENGGARA BARAT
Bibliographic record
Abstract
Penelitian ini menggambarkan struktur dan pola perubahan pertumbuhan sektoral dalam perekonomian. Penelitian ini juga di desain untuk menentukan sektor-sektor unggulan sehingga dapat dijadikan pertimbangan dalam perumusan kebijakan dan perencanaan pembangunan di Nusa Tenggara Barat (NTB). Selain itu penelitian ini juga dapat digunakan untuk melihat besarnya ketimpangan serta faktor yang menyebabkan terjadinya ketimpangan antar wilayah di provinsi NTB. Alat analisa yang di gunakan dalam penelitian ini adalah shift share dan Indeks Theil. Hasil penelitian menunjukkan bahwa dalam nilai National Share (N) tidak terdapat sektor-sektor ekonomi yang tumbuh lebih cepat dibandingkan sektor-sektor ekonomi yang sama di tingkat Nasional. Sementara itu berdasarkan Proportional Shift (P) terdapat sektor yang memiliki nilai negatif dan ada pula yang bernilai positif. Nilai Differential Shift tanpa migas menunjukkan tidak terdapat sektor-sektor ekonomi yang tumbuh lebih cepat dibandingkan sektor-sektor ekonomi yang sama di tingkat Nasional. Hasil Indeks Theil tanpa migas menunjukkan sangat merata. Namun apabila dengan migas menunjukkan nilai indeks Theil nya mendekati 1 yang berarti sangat timpang. Kata Kunci : Sumber-sumber ekonomi, Ketimpangan wilayah, Shift share, Indeks Theil
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".