Bibliographic record
Abstract
Today there is a distinction in Japanese Zen Buddhist monasticism between prayer temples and training centers. Zen training is typically thought to encompass either meditation training or public-case introspection, or both. Yet first-hand accounts exist from the Edo period (1603–1868) which suggest that the study of Buddhist (e.g., public case records, discourse records, sūtra literature, prayer manuals) and Chinese (poetry, philosophy, history) literature may have been equally if not more important topics for rigorous study. How much more so the case with the cultivation of the literary arts by Zen monastics? This paper first investigates the case of a network of eminent seventeenth- and eighteenth-century scholar-monks from all three modern traditions of Japanese Zen—Sōtō, Rinzai, and Ōbaku—who extolled the commentary Kakumon Kantetsu 廓門貫徹 (d. 1730) wrote to every single piece of poetry or prose in Juefan Huihong’s 覺範恵洪 (1071–1128) collected works, Chan of Words and Letters from Stone Gate Monastery (Ch. Shimen wenzichan; Jp. Sekimon mojizen). Next, it explores what the wooden engravings of Study Effortless-Action and Efficacious Vulture at Daiōji, the temple where Kantetsu was the thirteenth abbot and where he welcomed the Chinese émigré Buddhist monk Xinyue Xingchou (Shin’etsu Kōchū 心越興儔, alt. Donggao Xinyue, Tōkō Shin’etsu 東皐心越, 1639–1696), might disclose about how Zen was cultivated in practice? Finally, this paper asks how Kantetsu’s promotion of Huihong’s “scholastic” or “lettered” Chan or Zen might lead us rethink the role of Song dynasty (960–1279) literary arts within the rich historical context of Zen Buddhism in Edo Japan?
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".