MENAKAR INKLUSIVITAS KOTA DENGAN MENINJAU AKSESIBILITAS LAYANAN ESENSIAL BAGI LANSIA DI INDONESIA
Bibliographic record
Abstract
Penuaan penduduk menghadirkan tantangan penting terkait kebutuhan fasilitas kesehatan dan grocery shop bagi populasi lansia. Meskipun penelitian sebelumnya telah mengkaji dampaknya, masih terdapat kesenjangan dalam pemahaman mengenai ketimpangan dan inklusivitas lokasi fasilitas tersebut sesuai dengan persebaran lansia. Penelitian ini bertujuan untuk mengisi kesenjangan tersebut dengan memfokuskan pada penilaian ketimpangan (Gini ratio dan kurva Lorenz) dan keberlanjutan/inklusivitas (15 minutes city) fasilitas kesehatan dan grocery shop dalam melayani kebutuhan populasi lansia pada 3 Kota di Indonesia. Hasil analisis nearest neighbor menunjukkan adanya kecenderungan klasterisasi fasilitas di ketiga kota. Selanjutnya, analisis Gini ratio dan Kurva Lorenz mengungkapkan terjadinya inefisiensi akses fasilitas, di mana ineffisiensi yang tertinggi terjadi di Kota Malang (fasilitas kesehatan) dan Kota Bandung (grocery shop). Analisis isochrone menunjukkan bahwa inklusivitas tertinggi terdapat di Kota Yogyakarta dengan cakupan pelayanan fasilitas kesehatan sebesar 92,31% dan grocery shop sebesar 82,66%. Secara keseluruhan, penelitian ini menyoroti Kota Yogyakarta sebagai kota yang paling cocok bagi lansia dengan disparitas yang rendah dan inklusivitas yang tinggi, menjadikannya kota yang layak huni bagi lansia.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".