Bibliographic record
Abstract
E-commerce has evolved as a generation of technology that provides consumers and industries with the belief to process transactions. While China has gone through a period of pandemic, China's GDP in e-commerce has been growing steadily. In the forecasting model, there is an upward trend in the top five industries in China. And the larger percentage of wholesale and retail industries represents their importance in the future. Therefore, this paper will investigate the impact of e-commerce on China's economy by building two linear regression models. In building the linear regression models to find the relationship, different control variables are selected from various aspects, such as labor force, natural population growth rate, number of employed people and the proportion of enterprises with e-commerce transaction activities, to study their effects on the relationship between e-commerce and China's GDP. Through the research analysis, it is found that e-commerce has a significant positive impact on the Chinese economy, and securing the per capita disposable income of the population helps to promote the development of e-commerce, thus better promoting China's economic development for the better. Finally, this paper gives suggestions from both enterprise and government levels. Enterprises should develop suitable business strategies to meet consumers’ needs better. On the one hand, the government should encourage residents to consume while protecting consumers' rights and interests. On the other hand, the government should encourage enterprises to innovate and make appropriate policies to help them develop better.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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".