Analyzing China's Banking System as One of its Economic Rise Factors
Bibliographic record
Abstract
In 2013, China’s GDP grew at an average annual rate of nearly 10% than 1979. Many economists provide that China’s economic will take a key position in international trade map and a major place with the world leaders at beginning of second quarter of currant century. Generally, economists attribute China’s growth to two major factors: the financial system and the increasing of productivity. This paper tries to examine the role of China’s financial system in supporting the economic growth. Through giving, firstly, a short review of the history of China’s banking system by presenting it through 5 main periods: from 17th century until 1949, from 1949 to 1978, the first reform period from 1978 to 1984, the second reform period from 1984 to 1994, the third (present) reform from 1994. Secondly, it presents a brief view on the Structure of China’s Banking System that can be resuming in 4 main characteristics: (1) The Huge Size, (2) The State-owned, (3) The “BIG FOUR”, and (4) The Foreign Banks Operating in China. Then, it shows a short literature background about the relationship between financial and economic growth. Moreover, it discusses theoretical study of the influence of banking system on economic growth. Finally, this paper will analyze the role of banking system in supporting some indicators of China’s economic growth: GDP, Unemployment Rate, SME … etc.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".