The Ant Empire: Fintech Media and Corporate Convergence within and beyond Alibaba
Bibliographic record
Abstract
Data-driven technologies and platform economies have been widely employed by Chinese companies in a variety of business sectors. In the past decade, these digital applications have profoundly restructured economic development and power relations in Chinese society. This article examines how the promotions of fintech media and communication technologies for transactions, loans, investments and many other financial practices–engender and enlarge Alibaba’s digital financial platform, Ant Financial, the largest digital financial company in China. From a political economy of communication perspective, we consider Ant Financial as a product of data-centric corporate convergence in which fintech media have extracted user data from Alibaba’s e-commerce and digital payment platforms and utilized this data to drive Alibaba’s growth in financial businesses. The consolidation of multiple platforms has transformed financial industries, challenged policy and regulatory regimes, and reshaped the cultures of finance in China. The convergence paradigm underpins the digital, technology-driven logic facilitated by the state in resource allocation and policy-making. The government’s supportive role is a vital condition for the rise of Chinese fintech giants such as Baidu Finance, Ant Financial, Tencent Finance, and Jingdong Finance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".