PENGARUH PERKEMBANGAN INDUSTRI SKALA SEDANG DAN BESAR YANG TERAGLOMERASI TERHADAP PERMUKIMAN DI MOJOSONGO-TERAS, KABUPATEN BOYOLALI
Bibliographic record
Abstract
The development of medium and large scale industries are agglomerated in Mojosongo-Teras started in 2004, the development of the industry led to the pull of labor migration as a form of labor compliance, while also developing new economic activity. With the increasing population and the development of new economic activities, the settlement that have a role as a container that helped develop. The development of settlements in the area affected by industrial agglomeration characterized by an increasing number of buildings, increase the intensity of land use residential, residential facilities and service improvement. The problem in this research to know how to influence the development of medium and large scale industries are agglomerated against settlements in Mojosongo-Teras, Boyolali. The purpose of this study was to determine the effect brought about by the development of medium and large scale industries are agglomerated to the surrounding settlemets. The analytical method used in the method of scoring each sub variables of industrial and residential development, while also using the matrix method to determine the magnitude of the effect of the influence industrial development of the settlement. The result obtained are the development of medium and large scale industries are agglomerated in Mojosongo-Teras effect on the development of the settlement. High-level influence can be interpreted that the development of settlements in the area affected Mojosongo-Teras industrial agglomeration is strongly influenced by the conditions and level of industrial development in the region. Keywords: Agglomeration, Industrial Development, Settlement
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".