Bibliographic record
Abstract
Fintech is one of the most important research subjects nowadays. China’s financial computerizing has gradually realized the traditional business, which built a customer-centric electronic financial service system, and the rapid development of mobile banking, peer-to-peer payment services, and trading platforms. Under the backdrop of the rapid development of financial computerization, there are a series of clear and potential risks in financial computerization. Therefore, the research topic of this paper is the impact of today’s digitalization on the development of financialization. This paper will collect the data of today’s digital financialization application and analyze these data from the perspective of finance. At present, the financial industry and technological innovation are increasingly closely linked, and technology-driven financial innovation covers the whole world. In addition, the COVID-19 pandemic is accelerating the global digitization process, accelerating the competitive development of global big data and digital economy. As isolation measures increase the demand for telecommuting and online education, the global demand for broadband communications services has soared. Meanwhile, the consumption of content based on short videos and live broadcasts has soared, resulting in a rapid increase in the amount of information created and captured worldwide. As a result, the progress of fintech has promoted the great prosperity of the financial industry.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".