LEADING BUSINESS SECTORS THEIR RELATIONSHIP FOR REGIONAL DEVELOPMENT IN TEGAL REGENCY
Bibliographic record
Abstract
The emergence of Covid-19 has impacted changes in the economic structure of leading business sectors and regional development in various areas. The purpose of this study is to determine the leading business sectors in Tegal Regency before and after the Covid-19 pandemic. Berbeda dengan penelitian sebelumnya, penelitian ini juga menekankan analisis regional development in Tegal Regency. Descriptive research with a quantitative approach is used in this study. Data sources were obtained from the Central Bureau of Statistics (BPS) in the form of ADHK Gross Domestic Regional Product (GDRP) by business sector in Tegal Regency and Central Java Province, covering the last 8 (eight) years, from 2016 to 2023. The data analysis techniques used are Location Quotient (LQ), Shift-Share (SS), and Klassen Typology to identify leading sectors based on their quadrant analysis. There are 4 (four) leading business sectors in Tegal Regency, namely Mining and Quarrying; Accommodation and Food and Beverage Services; Information and Communication; and Education Services. This analysis indicate that business sectors are linked to regional development in Tegal Regency. These findings confirm a shift in the economic structure after the pandemic, which opens opportunities for strengthening the Information and Communication sector as well as the Education Services sector. The policy implication for the local government is to formulate economic recovery strategies based on leading sectors that are adaptive to structural changes. The results of this analysis indicate that the fourive business sectors are linked to regional development in Tegal Regency.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".