Bibliographic record
Abstract
Indeks Pembangunan Manusia (IPM) digunakan untuk mengukur capaian pembangunan manusia berbasis sejumlah komponen dasar kualitas hidup. Sebagai ukuran kualitas hidup, IPM dibangun melalui pendekatan tiga dimensi dasar. Dimensi tersebut mencakup dimensi kesehatan, dimensi pengetahuan, dan dimensi standar hidup layak. Kondisi IPM Kota Malang dari tahun ke tahun selalu mengalami peningkatan. Analisis IPM selalu terkait dengan indikator komposit penyusunnya. Keseluruhan indikator komposit berkontribusi terhadap capaian nilai IPM, tetapi besaran kontribusi dari masing-masing indikator tersebut perlu dilakukan pengukuran. Pengukuran seberapa besar signifikansi pengaruh dari masing masing indikator komposit terhadap nilai IPM sangat diperlukan untuk menentukan prioritas program yang harus dilakukan terlebih dahulu, di tengah kondisi pandemi covid-19 yang mengharuskan untuk efisisensi anggaran dan pembiayaan. Variabel usia harapan hidup, harapan lama sekolah, rata-rata lama sekolah, dan pengeluaran per kapita mempunyai korelasi yang sangat tinggi dan signifikan terhadap nilai IPM. Analisis regresi linier berganda digunakan sebagai salah satu metode untuk menentukan analisis pengaruh indikator komposit IPM terhadap nilai IPM Kota Malang.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".