The Soft Power of American Missionary Universities in China and of their Legacies
Bibliographic record
Abstract
This paper analyses the historical ‘direct’ soft power of American missionary universities in China and their ‘reverse’ soft power towards American society until their nationalization in the early 1950s. The paper also addresses the soft power of the legacies of these historical universities. This analysis is based on the cases of St. John’s University, Yale-in-China and Yenching University.\nAmerican missionary universities were founded with the clear ‘direct’ soft power purpose of attracting the Chinese ‘other’ to Christianity. However, soft power resources often have unintended behavioral consequences and a particularly interesting one is ‘reverse’ soft power: Where the intended object society of soft power influences the originator society of soft power, for example, through education and advocacy. American missionary universities exercised substantial soft power both toward the Chinese host society and toward the American society. The institutions in China also left institutional legacies at American—and Canadian—universities which continue to hold soft power in the relationship between American and Chinese society. The extent and limitation of this bidirectional soft power can be discerned from what attracted different actors to these universities and what those and other actors rejected about these universities; this is based on a detailed analysis of the relations between the universities and different public and private actors in the host society and the society of origin. These relations were characterized by the role of the universities as bridges between host society and society of origin carrying much information between societies, raising awareness and interest about the other society, moving elite-level human resources back and forth and raising large financial resources in the USA for education and research in China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".