ANALISIS PENGARUH SEKTOR PERDAGANGAN TERHADAP PDRB SUMATERA UTARA DENGAN MENGGUNAKAN METODE LOCATION QUOTIENT
Bibliographic record
Abstract
Regional economic growth is one of development indicator which is closely related to the level of people's welfare. One of the components used to measure economic growth is the Gross Regional Domestic Product (GRDP). In GRDP, it can be seen which of the economic sector that contributes the most. The purpose of this study is to analyze the specialization of the economic sector, especially trade sector, to be developed in North Sumatra Province. The data used in this study is secondary data in form of data on the Gross Regional Domestic Product (GRDP) of North Sumatra Province in 2016-2021 which is processed using the Location Quotient (LQ) approach. The result showed that the Agriculture, Forestry, and Fisheries sectors; Real Estate; and Trade are sectors that have more influence than other regions nationally, so these three sectors need to be the government's attention. The trade sector which is the sector with number 3 specialization in North Sumatra compared to other regions nationally is an interesting thing to be highlighted because it is the sector with the largest tax revenue in North Sumatra and the GRDP of the sector always grow except in 2020 due to the Covid-19 Pandemic.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".