Exploring correlations between economic indicators with natural and societal factors based on linear regression model
Bibliographic record
Abstract
Existing research on the determinants of a nation’s economic development has predominantly centered on individual factors, including energy, land resources, education, taxes, employment, and healthcare. Regrettably, there is a paucity of studies that holistically examine these factors collectively and assess their respective contributions to economic development. Therefore, the primary objective of this study is to investigate the interrelationships between economic indicators and various natural and societal factors. The article firstly uses the Pearson’s correlation coefficient to filter out a portion of the higher degree of correlation from factors that may have an impact on the country’s economic development for further analysis. For the selected factors, using two linear regression models: Ordinary Least Square (OLS) method for preliminary modeling for the extent of affects between each factors and economy; and Fully Modified Ordinary Least Squares (FMOLS) method, as an optimization model, further eliminating the less influential variables. After obtaining the final impact model of the linear correlation, the data is screened based on the variables within the model. A portion of the selected data is used as a training set for training the model and the remaining data is used as a test set for testing the performance. The results of the study show that factors including land area, army size, CO2 emissions, population, minimum wage, would have varying degrees of integrated impact on the economic development of the country.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".