Perkembangan dan Karakterisasi Desa-desa Pegunungan Jawa Tengah
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
The characteristics of mountain villages are very different from valley villages and plain villages, but socio-economically and environmentally related to each other. This study aims to analyze the level of development of physical facilities in mountain villages, analyze the village development index based on the dimensions of village development, and analyze the components of socio-economic, environmental, and developmental characteristics of mountainous villages in Central Java. Analysis of the level of development of mountainous village physical facilities used skalogram based on PODES 2018 data, village development index based on the dimensions of village development used the Village Index (ID) calculation formula, and analysis of the characteristics of the socio-economic, environmental, and developmental components of mountain villages used Principal Component Analysis (PCA). Results of the analysis of the level of development of the physical facilities of the mountainous villages show that 413 villages (67.81%) of the mountains are in the third hierarchical class (less developed). The category of village development based on the dimensions of development shows that mountain villages are included in the category of developing villages with an average value of ID 54.17. The components that best characterize the characteristics of mountainous villages are the potential for the danger of 21.9%, the availability of secondary school education facilities, health facilities, and the village development level of 16%, the component of trade facilities 5.8. %, the component of the availability of the micro-industry is 13.25%, and the component of the availability of health facilities are 8.8%.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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