China's ‘Christian General’ Feng Yuxiang, the Evangelist Jonathan Goforth and the Changde Revival of 1919
Bibliographic record
Abstract
General Feng Yuxiang (1882–1948), China's ‘Christian General’, had already been a Christian for about six years before he decided systematically to evangelise his troops while they were stationed in northern Henan. He was convinced that Christianity would save his men and, in the process, would save China. To this end, Feng invited the Canadian missionary Jonathan Goforth (1859–1936) to hold a remarkable series of revivals in the late summer of 1919. During these revivals, which were modelled on the work of the evangelist Charles Finney, Feng himself broke into prayer in front of his men, and eventually 507 of Feng's troops were baptised. By the time of Goforth's second visit to Feng – a little over a year later – over 5,000 of the 9,000-man brigade had been baptised. This study will rely on Goforth's journal from 1919, Feng's own diaries, and other material to see how Goforth and Feng worked together to Christianise a significant segment of Feng's army. So did the ‘Christian General’ ultimately form a ‘Christian Army’ or even an indigenous church? Did Goforth's revivals in Feng's army have any long-term effect? Was Feng a convinced Christian, a Chinese patriot or simply an opportunist? This study seeks to answer these questions. 1
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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 | 0.001 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".