Bibliographic record
Abstract
Unlike the depth of international political economy (IPE) research on finance and money in North America and Britain/Europe, or the amount of work that has been done inside China on the IPE of international trade, the IPE of global finance and money is still at a nascent stage inside China. The paper examines the evolution of Chinese IPE research on global finance and money and suggests that research in these issue areas appears to be reaching a turning point. The main empirical finding is that this shift in knowledge production has been induced principally by China's emergence as a financial force and the national developmental concerns this entails, as well as by the onset of the 2008–09 global financial crisis and the rise of the emerging economies’ grouping. The growing Chinese scholarship on the IPE of finance and money is adding analytical depth and broadening Chinese IPE, particularly on the impact of financial globalization on developing and emerging economies. While such research will likely contribute to Chinese policymaking in the future, the scholarly test for Chinese IPE is whether and how it will contribute to filling the global knowledge gaps on the determinants of financial and monetary policy, and whether it will give rise to new understandings on global finance and money, especially the causes of international financial crises. Heretofore, much of the literature has been heavily policy-oriented and normative.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".