Transformasi Wilayah Kabupaten Demak Sebagai Kawasan Pinggiran di dalam Proses Metropolitanisasi Semarang
Bibliographic record
Abstract
The availability of regional road network, as an infrastructure for accommodating activities and interregional interactions has made the areas of Demak Regency divided into several areas according to the differences in dynamics of their development process. All areas in Demak Regency have transformed gradually with some different level dynamics in each area. It is important to understand the transformation process in the district including in each area. This study aims to examine the regional transformation process in each area in Demak Regency and find out which areas are undergoing rapid transformation or development than others. Land use transformation is analyzed by using the Maximum Likelihood and Calculate Area analysis techniques in Arcgis. Population transformation analyzed using quantitative descriptive analysis techniques and economic transformation analyzed using LQ analysis techniques and descriptive analysis. The result shows that the south-west and north-west areas, which are bordering to the Metropolitan City of Semarang and traversed by regional road network tend to have a more dynamic transformation than other areas that are more far from the city. There are some factors that bring some areas of Demak Regency to develop rapidly, such as the proximity to the metropolitan city, the availability of regional road networks, and the development of activities in the area. It is also found that the formulation of the RTRW for Demak Regency has not taken the different dynamics of development of areas in the district into account. So, there are some areas that need more attention from the Government of Demak Regency, like the east-north and central areas whose development is still under the level directed in the RTRW, so that their development needs to be encouraged. as a result of the formulation that has not paid attention different dynamics in each areas.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".