Research on the promotion effect of supply chain finance innovation on regional economy based on blockchain technology
Bibliographic record
Abstract
Supply chain finance innovation has a significant impact on regional economy.In this paper, blockchain technology is applied to supply chain finance business to improve the technology and security of traditional supply chain finance business.Drawing on relevant research results, we construct a blockchain-based supply chain financial innovation efficiency evaluation index system and measure the supply chain financial innovation efficiency using Malmquist index.A spatial econometric model is used to test the spillover effect and spatial synergy between supply chain financial innovation and regional economic growth, and to demonstrate the promotional effect of blockchain-based supply chain financial innovation on regional economic growth.The centres of the distribution curves of the kernel density function of the logarithmic value of GDP and supply chain financial innovation of the 30 provinces and regions are all shifted to the right, and the height of the main peak rises gradually.The 2013-2023 regional Moran's index of economic growth and supply chain financial innovation are both significantly positive.The regression coefficients of supply chain financial innovation under the two spatial weights are significant at the 1% level, which provides strong data support for the view that supply chain financial innovation can promote regional economic growth in this paper.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".