Analisis Regresi Spasial Persentase Kemiskinan di Kawasan Timur Indonesia Tahun 2022
Bibliographic record
Abstract
Kemiskinan merupakan masalah multidimensional yang terjadi di Indonesia. Kemiskinan erat hubungannya dengan tingkat ketahanan pangan di suatu daerah. Kawasan Timur Indonesia (KTI) menjadi perhatian karena tingginya tingkat persentase kemiskinan. Tujuan dari penelitian ini adalah melihat variabel penyusun ketahanan pangan yang berpengaruh signifikan terhadap persentase kemiskinan tingkat kabupaten/kota di Kawasan Timur Indonesia. Identifikasi heterogenitas spasial yang dilakukan menunjukkan penggunaan model Geographically Weighted Regression (GWR) lebih baik dibandingkan regresi linier berganda (RLB) untuk menunjukkan hubungan antara variabel penyusun ketahanan pangan terhadap persentase kemiskinan. Dihasilkan model GWR lokal untuk melihat heterogenitas spasial pada level kabupaten/kota dengan fokus variabel rasio konsumsi normatif perkapita (NCPR), persentase rumah tangga dengan proporsi pengeluaran untuk pangan lebih dari 65% terhadap pendapatan (PP), dan kombinasinya dengan variabel lain. Diperoleh bahwa tingkat kemiskinan pada level kabupaten/kota di Kawasan Timur Indonesia dipengaruhi oleh variabel penyusun ketahanan pangan yang berbeda-beda, terutama antara variabel PP dengan akses listrik dan angka harapan hidup.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".