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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".