Analysis of Leading Sectors and Characteristics of Provincial Economic Growth in Indonesia
Bibliographic record
Abstract
Gross Domestic Product or GDP is one indicator of a country's economy moving the economic sector. The Covid-19 pandemic caused the economy of Indonesia in the second quarter of 2020 compared to the second quarter of 2019 (YoY) to experience a 5.32 percent contraction. In increasing the economic growth rate, adjusting it to each region's potential is necessary, which is the leading sector. This study aims to analyze the basic or leading sectors and the economic growth conditions of the provinces in Indonesia and group the provinces based on their proximity characteristics. The method used is the Ward and Location Quotient using secondary data from Statistics Indonesia. The results show that 25 provinces in Indonesia have an agricultural base sector. The mining sector is primarily concentrated in eastern Indonesia and tends not to affect the Covid-19 because it can grow fast. Provinces with a manufacturing industrial base sector are focused on Java. The clustering results formed 5 clusters, each province having similar characteristics. The growing cluster is a province with a mining base sector. The sector with intense contraction is a province that provides accommodation, food, and drink. Local governments can restore the economy starting from the base sector.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".