KEKUATAN SPASIAL KLUSTER PERKOTAAN DI KABUPATEN SEMARANG
Bibliographic record
Abstract
Semarang Regency has 19 sub-districts with diverse regional growth. Several areas have experienced development into urban areas, such as Bawen District, East Ungaran District, West Ungaran District, Bergas District, Pringapus District, and Ambarawa District. Each sub-district has urban areas with different levels of influence. This research aims to identify urban clusters that are formed in the five sub-districts that are the focus of the study and provide an assessment of the urban areas that have the greatest influence. The research method used is quantitative descriptive. Secondary data collection is carried out through documentation techniques, namely by recording and studying statistical data relevant to the problem being discussed. Urban clusters in West Ungaran and East Ungaran Districts reciprocally provide the greatest spatial influence. The spatial interaction between the Bawen and Ambarawa urban clusters is in second place. The smallest spatial flow occurs between urban clusters in Bergas District and East Ungaran District. It is hoped that this research can provide input to the regional government of Semarang Regency to increase social interaction between Bergas District and Ungaran Timur District, through investment activities and development of several areas for industrial activities or other activities that can increase the attractiveness of the two clusters in these two sub-districts
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".