Factors Affecting Economic Growth in West Sumatera Province Using Panel Data Regression Analysis
Bibliographic record
Abstract
Economic development is considered successful if the economic growth rate of its people reaches a high level. Indonesia has positive economic growth with economic growth rates above 5% in each quarter. However, the high economic growth of Indonesia does not mean that all regions have the same growth rate. Where the increase in the number of goods and services received or the added value of production factors is often referred to as economic growth. Regional economic growth in West Sumatera Province is known to tend to be negative. This study aims to obtain an overview of panel data regression models and factors that have an influence on economic growth in West Sumatera Province for the period 2018 to 2022. The best regression model obtained is the fixed effect model (FEM), where at a significant level of 5%, the factors that have an influence and positive relationship on economic growth are the human development index and government spending.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".