Empirical Analysis on Regional Economic Growth from the Perspectives of Entrepreneurship and Investment in Research and Development
Bibliographic record
Abstract
This paper mainly assesses the influence of entrepreneurship over regional economic growth (REG) and the investment in research and development (R&D), and puts forward countermeasures to promote the REG. The fixed effects model for panel data analysis was adopted to evaluate how entrepreneurship directly affects REG and indirectly affects REG via R&D investment. The results show that the entrepreneurial and innovative spirits of entrepreneurs promote economic growth; the promoting effect of entrepreneurial spirit is greater than that of innovative spirit; R&D investment mediates the influence of entrepreneurship over economic growth; the three major economic regions of China, namely, East, Central, and West China, have different laws in the influence of entrepreneurial spirit and innovative spirit on economic growth and R&D investment, owing to the variation in resources endowment and entrepreneurship allocation. The research results help policy-makers formulate regional economic development strategies in the light of entrepreneurship configuration and R&D investment.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".