Quantitative Evaluation of County-level Economic Development in Henan Province Based on Multiple Statistical Regression Models
Bibliographic record
Abstract
County economic development directly affects the national economy, and the county economy of Henan Province has become the economic pillar of the province.The purpose of this paper is to analyze the county-level economic development of Henan Province and its economic influencing factors by using the quantitative evaluation method.From the time series, the level of economic development of 105 county units in Henan Province from 2000-2023 is analyzed from two perspectives, absolute difference and relative difference, using the indicator of GDP per capita.Screening of factors affecting the level of economic development of counties in Henan Province is carried out from the aspects of population, resources, policies, etc., and a four-aspect indicator system is constructed, namely, human capital, government regulation, industrial level, and economic vitality.A multiple linear regression model is established, and the regression model is fitted by the regression coefficients of each influencing factor, and the fit of the regression model is examined.Each county in Henan Province is divided into three development gradients: developed, generally developed and less developed counties.Panel data regression analyses were conducted on the overall county economy of Henan Province and the influencing factors of developed, generally developed and less developed counties respectively.In the overall economic development of counties in Henan Province, the degree of influence of physical capital investment and the structure of secondary and tertiary industries on the overall differences in county economies is particularly significant.It is manifested in the fact that for every 1% increase in the investment in fixed capital of the whole society, the output of GDP per capita increases by 0.09112% accordingly.Therefore, in order to improve the differences in the economic development of counties in Henan Province, local governments and enterprises should make efforts to improve the market and investment environment and adjust the structure of secondary and tertiary industries.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".