ANALISIS PENGARUH SEKTOR PERDAGANGAN TERHADAP PDRB SUMATERA UTARA DENGAN MENGGUNAKAN METODE LOCATION QUOTIENT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it