Christian mission and education in modern China, Japan, and Korea : historical studies
Bibliographic record
Abstract
Contents: Scott W. Sunquist: American Christian Mission and Education: Henry W. Luce, William R. Harper, and the Secularization of Christian Higher Education - Jan A. B. Jongeneel: Christian and Missionary Education in the Netherlands and in Indonesia as Challenge - Stuart Macdonald: Religion and Secularization in Canada: Education and the Impact on Mission - Peter Tze Ming Ng: From Cultural Imperialism to Cultural Exchange: Christian Higher Education in China Revisited - Jiafeng Liu: Religious Education in Christian Colleges in pre-Communist China: Challenges and Renovations - Yihua Xu: Birth, Growth, and Decline of the Chinese Student Volunteer Movement for the Ministry in 20th century China - Feiya Tao: Christian Colleges in China: New Relations and New Perspectives since the 1980s - Takaaki Haraguchi: David B. Schneder's Idea of Christian Education and its Implementation in Face of the Nationalistic Education in Modern Japan - Rui Kohiyama: Women's Education at Mission Schools and the Emergence of the Modern Family in Meiji Japan - Chae-ok Chun: Rediscovering Ewha Mission and its Contribution to Education - Sung-jeon Lee: Empire, Moral Superiority, and Mission Schools: The Establishment of Sungsil School and College in Early 20th Century Pyeongyang, Korea - Dong-min Chang: Crises and Prospects of Mission Schools in Contemporary Korea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".