Classifying Economic Sectors to Improve Regional Development Priorities in Indonesia
Bibliographic record
Abstract
The main aims of the study are to classify economic sectors and compare development priorities in an Indonesian district to determine suitable programs.Gross regional domestic product data for 2011-2022 was analyzed using static location quotient, dynamic location quotient, and shift-share analysis.The results of the study showed that the district's economic sectors were classified into mainstay, leading, and potential sectors.The mainstay sector consists of electricity and gas procurement, construction, wholesale and retail trade, car and motorcycle repair, and transportation and warehousing.The leading sectors are education services, manufacturing, information and communication, and financial and insurance services.The other nine sectors are potential sectors: agriculture, forestry, and fisheries; mining and quarrying; water procurement, waste management, waste and recycling; provision of accommodation and food drink; real estate; corporate services; government administration; defense, and compulsory social security; health services and social activities; and other services.The study implies that mainstay sectors are suitable as regional development priorities.Leading sectors can be the second priority.Potential sectors are not suitable as priorities for regional development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".