Steering FinTech: Techno-industrial policy for the data-driven economy in China’s Greater Bay Area
Bibliographic record
Abstract
China has emerged as a global force in the digital economy, its rapid technological advancement challenging the technological leadership of the West and generating geopolitical-economic tensions. Positioned at the cutting edge of innovation, financial technology - commonly referred to as FinTech - has emerged as a key terrain in this battle for financial and technological dominance. China's rapid ascendance in this sphere raises questions about the political-economic drivers and conditions of existence of such transformations. While the literature on FinTech examines market-driven, industry- and firm-specific processes transforming global financial networks, less is known about the roles of the state in steering the integration of technological and financial systems as a national development strategy. This paper analyzes how the Chinese party-state under Xi Jinping is implementing a techno-industrial policy centered around data as a new factor of production, seeking to ascend the industrial value chain and compete at the technological frontier. Focusing on the Greater Bay Area region - a preeminent innovation economy and financial hub - the paper examines the policy mechanisms, local economic spaces and practices grounding this digital economy, shaped by the interplay between state interventionism and the market-driven allocation of resources.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 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".