The Impact of Digital Economy on Traditional Economic Models in China and The United States
Bibliographic record
Abstract
With the rapid development of digital technology, the digital economy has become an important engine of global economic growth. As leaders in the global digital economy, China and the United States have their own characteristics in terms of technological innovation, market size and policy environment. At the same time, the digital economy has had a profound impact on the traditional economic model. This paper compares and analyzes the impact of the digital economy of China and the United States on the traditional economic model, and explores their similarities and differences in industrial structure, employment market and business model. The study found that the digital economy of China and the United States has promoted the upgrading of traditional industries, improved production efficiency, and spawned new business models such as platform economy. However, the United States pays more attention to original technology research and development, while China has achieved rapid popularization of the digital economy with a huge user base and policy support. In addition, China and the United States face common challenges in data security, market competition and the digital divide. In the future, the digital economy of China and the United States will continue to deepen competition and cooperation in technological innovation, globalization and sustainable development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".