Rural e-commerce platform data analytics for economic development
Bibliographic record
Abstract
This paper firstly studies the current situation of rural e-commerce development in China, and then collects the gross output value of agriculture, forestry, animal husbandry and fishery, express delivery volume, rural delivery routes and so on through consulting the relevant official data of the National Bureau of Statistics, which provides an effective and reliable data basis for the construction of econometric model.Through the establishment of a fixed-effects model to analyze the empirical results, to explore the role of rural e-commerce platform development on the promotion of the economy.Finally, with the help of the spatial Durbin model to measure the spatial spillover effect, analyze whether the development of rural e-commerce can reduce the urban-rural income gap.The results show that the number of Taobao villages, kilometers of rural delivery routes, and 10,000 rural broadband access users are the explanatory variables, and the gross output value of agriculture, forestry, animal husbandry and fishery is the explanatory variable, and the coefficients are 0.0156, 0.0781, and 0.0442, with the p-value less than 0.01.Therefore, the better the development of rural ecommerce, the better the economic development is.And the increase in the level of economic development can significantly reduce the urban-rural income gap with an estimated parameter of -0.022.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".