Component Driven of Regional Competitiveness for Urban Development in Central Java Province Indonesia.
Bibliographic record
Abstract
Abstract Economic growth in Central Java Province is currently being driven by the rapid acceleration of investment, leading to the development of cities and districts. However, the measurement of regional competitiveness is currently limited to analysing regional capabilities in development. This condition creates a situation where regencies and cities compete with each other without considering the functions and roles of interconnected regions. Therefore, there is an urgent need to understand and utilize regional competitiveness within the framework of the regional system in order to support the policies of the Central Java Province. This study adopts a quantitative approach to analyse the stages of regional development, which are identified based on competitiveness starting from basic requirements as a factor-driven, efficiency-driven, and innovation factor. Using this approach, we aim to classify the functions and roles of cities and districts within the regional system in Central Java. The data for this study was obtained through the distribution of questionnaires in 35 districts and cities in Central Java Province, as well as interviews with stakeholders from both the regions and the provinces. The findings of this study reveal that cities and regencies have different stages of competitiveness, with some focusing on driving innovation and strengthening driving factors, while neglecting efficiency. Spatially, cities exhibit a higher level of competitiveness; however, most urban districts have not incorporated the role of the region in the planning of a sustainable regional system.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".