Making Rural Finance in Contemporary China: National Policies, Local Practices, Geographical Embeddedness
Bibliographic record
Abstract
Since the 2000s, the changing global financial landscape has attracted increasing attention from geographers, particularly after the 2008 financial crisis. China’s rural banking sector, still a marginal site in research on the geographies of finance, is a fast-growing financial frontier. In the decade since 2008, bank credit issued to the rural sector has increased six fold to 4.6 trillion USD in 2018. The rapid expansion of this financial frontier owes much to ongoing reforms led by the Chinese state. Since 2003, the Chinese government has been initiating a series of market-based reforms aimed at building a “modern rural financial system” to better support farmers, agriculture and rural development. This dissertation explores the changes that have reshaped the banking system in rural China as a distinct case of financialization. Building on economic geography scholarship that argues the need for financialization to be understood as place-specific social processes, this research adopts a multi-scalar approach. I link a macro-level political economic analysis of China’s rural financial policies with ground-level observation of financial practice. Primary research methods include ethnographically-informed research in a rural bank in Greater Chengdu Area (Sichuan Province), and textual analysis of policy documents and historical archives. The research finds that the transformation of the banking system in rural China that began in the early 2000s has an internal logic shaped by the political economic conditions of contemporary China, with traces of the ideals and practices of socialist development in China. Given such, China’s rural financialization cannot be framed as following a straightforward neoliberalization process, the often-applied meta-frame for financialization. Rather than providing a local variant of neoliberal globalization, I argue that financialization in rural China needs to be understood as a localized, contextualized process – the trajectory of which is shaped by contested logics operating at multiple scales.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".