ANALISIS PERANAN KATEGORI EKONOMI BASIS DAN EFISIENSI PERTAMBAHAN INVESTASI DI KABUPATEN MINAHASA UTARA
Bibliographic record
Abstract
This research aims to analyze the role of the categorical or economic base sector as well as to observe the efficiency of investment accumulation in North Minahasa Regency. This research was conducted in the area of North Minahasa Regency, North Sulawesi Province. The study began in April until October 2018. This research was conducted in the area of North Minahasa Regency, North Sulawesi Province. The study began in April until October 2018. This research employs secondary data from Regional Gross Domestic Product (RGDP) based on the constant price in North Minahasa Regency and North Sulawesi Province and the data from The Change of Regional Gross Fixed Capital in North Minahasa Regency. The instrument used in this research is Location Quotient (LQ), Shift Share Analysis, and Incremental Capital Output Ratio. Results from LQ show that the category of agriculture, forestry, and fishery, mining and excavation, manufacture, construction, electricity and gas, real estate and education service serve the base category in North Minahasa Regency, with the value of LQ above 1. The role of base category shows positive result towards the formation of the Regional Gross Domestic Product in North Minahasa Regency, agriculture and forestry and fishery are the biggest contributor in RGDP of North Minahasa Regency during the period of 2013-2017. The role of base category through regional share towards North Sulawesi Province also shows positive results, thus base category in North Minahasa Regency contributes to the formation of RGDP in North Sulawesi Province. In the calculation of proportional shift, several base categories in North Minahasa Regency received negative values, namely agriculture, forestry and fishery, manufacture, and education service. Then, in the calculation of differential shift, electronics and gas is the only sector which receives negative value or is not able to compete with similar category in the provincial level. Also, the calculation of Incremental Capital Output Ratio as the instrument of the efficiency of capital income in North Minahasa Regency in the period of 2013-2017 which is calculated by the standard method to lag0, lag1 as well as the mean calculation method, show the result of ICOR which can be categorized as not efficient.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".