Analisis Faktor-faktor yang Mempengaruhi Indeks Pembangunan Manusia Kabupaten Malang Berbasis Pendekatan Perwilayahan dan Regresi Panel
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Regionalization approach is a kind of approach to manage and to achieve the development goals based on the characteristics of a region. The development system of Malang Regency is conducted through regionalization approach which divided the area into six Development Areas (WP). Furthermore, the typology of development area in Malang Regency can be divided into three typology (urban, peri-urban and rural). This research aims to analyze factors that influence the human development index (HDI) of Malang Regency based on regionalization approach and panel regression. Data panel regression method was used in analyzing the data. The results of the research showed variables that have a positive and significant influence to the human development index in each typology of development areas of Malang Regency consisting of the number of health facilities, the number of nurse-midwife and the population density in typology I (urban); the ratio of school per students at primary school and the population density in typology II (peri-urban); and the number of nurse-midwife in typology III (rural).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it