Zhu Shunshui (1600-1682) – the influence of, on and via him during his lifetime
Bibliographic record
Abstract
Of the many Chinese who sought refuge in Japan during the middle of the seventeenth century, Zhu Shunshui 朱舜水 (1600-1682) is perhaps one of the most talked about. He fled China in 1645. In 1659, giving up all hopes on the restoration of the fallen Ming dynasty 明朝 (1368-1644) after fifteen-year’s unfruitful efforts, Shunshui decided to sojourn in Japan and sworn not to return until the Manchu 滿族 regime is driven out of China. During his stay in Japan, the prominent Mito 水戶domain lord Tokugawa Mitsukuni 徳川光圀 (1628-1701) hired him as his teacher. Mitsukuni is the founding father of the Mitogaku 水戸学, one of the most important schools of thought in Edo period (1603-1867). This school aimed to reconstruct the historiography of Japan by Chinese Neo-Confucianism principles so as to promulgate indigenous Shinto beliefs and absolute loyalty to the emperor. The close relationship between Shunshui and Mitsukuni, and the involvement of Shunshui’s students in projects initiated by Mitsukuni, including the compilation of the Dai Nihon Shi 大日本史 (The History of the Great Japan, 1906), make some scholars believe that Shunshui has dominant influence on Japan’s Neo-Confucian thoughts, if not all Edo thoughts, as well as far-fetching inspiration on Meiji Ishin 明治維新 (the Meiji Restoration, 1868). But the flow of information among brains and its effect on recipient is too dynamic to be measured. Cultural influence over time is even more difficult to trace. By investigating Shunshui’s relationship with different people and his involvement in various events in Japan during his life time, this paper aims to clarify whether the general beliefs on his influence are plausible. In case when the findings are negative, the paper will look into the causes and suggest where Shunshui’s should be.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".