Economic Forecast Based on Statistical Economic and Population Development Targets Based on Mobile Communication Technology
Bibliographic record
Abstract
Mobile communication technology is a universal technology. It is one of the most advanced technologies in human history due to its rapid development speed, strong penetration and wide application. The rapid development and wide application of information and communication technology has brought profound impact on economic growth and social transformation. In order to test the relationship between population change and economic development in the process of urban development by mobile communication technology, cluster analysis and similarity coefficient are used to analyze the indicators of economic development and to predict the law of economic development. In order to understand the changes brought by mobile communication technology to economic development in detail, through the analysis of the usage of mobile communication users in China and the economic development of the region from 2017 to 2019, the results showed that Beijing was in the best development situation. Compared with other regions, Tibet was the lowest. It grew over time, with a 5% increase from 2017 to 2019. It could be seen from this point that vigorous development in the field of mobile communications would provide new opportunities for major cities to break through development bottlenecks and solve development dilemmas as well as promote urban transformation and innovative 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".