Nankai school: The experience of adapting economics to Chinese conditions in the 1920s–1930s
Bibliographic record
Abstract
Using the example of the activities of the Nankai Institute of Economics in the second quarter of the twentieth century, the article analyses the problem of adapting Western economic theories to the study of the Chinese economy. At the heart of the program of sinicization of economic research and education proposed by the Nankai school was the work of collecting and systematising reliable information on the Chinese economy. In the second half of the 1920s, Nankai University became a leader in China in conducting socio-economic surveys, compiling index numbers of prices, studying selected industries and rural regions. The founders of the Nankai school. He Lian and Fang Xianting were educated in economics in the United States; up until the late 1940s, the Nankai Institute of Economics was highly dependent on American grant support. This did not prevent them from setting the objectives of “knowing China” and “serving China” by combining foreign theories and methods with an understanding of the real economic situation based on reliable quantitative data. The task of “localization” of economics stimulated writing of pioneering university textbooks that explained general theoretical concepts through Chinese examples. Focus on solving China’s problems led the economists to abandon copying ready-made foreign prescriptions. During the two decades of activity in the Republican period the Nankai school made major achievements in collecting factual material on Chinese economy and adapting courses, its legacy has become an important starting point of the contemporary policy of sinicization of economics in the PRC.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.017 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".