Analisis Determinan Pembangunan Ekonomi Inklusif di Provinsi Kalimantan Selatan Menggunakan Pendekatan Panel Vector Error Correction Model (PVECM)
Bibliographic record
Abstract
Keberhasilan pembangunan ekonomi diukur melalui tingkat pemerataan dan keberlanjutan, khususnya melalui Indeks Pembangunan Ekonomi Inklusif (IPEI) yang menekankan pertumbuhan ekonomi merata untuk seluruh lapisan masyarakat. Penelitian ini menganalisis hubungan antara IPEI dengan faktor-faktor yang mempengaruhinya, yaitu Indeks Pembangunan Manusia (IPM), Pertumbuhan Ekonomi (PE), dan Persentase Penduduk Miskin (PPM), menggunakan Pendekatan Panel Vector Error Correction Model (PVECM) di Provinsi Kalimantan Selatan selama 2011-2021. Hasil penelitian menunjukkan bahwa data terintegrasi pada tingkat yang sama, dan terdapat persamaan terkointegrasi dengan optimal lag-5, yang berarti bahwa dampak perubahan IPM dan pertumbuhan ekonomi terhadap IPEI dapat mempengaruhi IPEI hingga lima periode waktu. Estimasi PVECM menunjukkan bahwa IPM dan pertumbuhan ekonomi secara signifikan mempengaruhi IPEI dalam jangka panjang dan pendek. Dalam jangka panjang, peningkatan IPM dan pertumbuhan ekonomi berdampak negatif pada IPEI. Sementara dalam jangka pendek, perubahan IPM dan pertumbuhan ekonomi dapat memengaruhi IPEI hingga lima periode ke depan.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".