Discussing "The tale of the Heike" in the Edo period : didactic commentaries as guides to wise rule for warrior-officials
Bibliographic record
Abstract
In the premodern period, the Tale of the Heike (thirteenth century CE) was regarded either as a source for popular entertainment, such as musical and performing arts, or a historical text used for scholarly purposes. Most studies on the Tale of the Heike’s reception have focused on the work’s literary and artistic side, while scholarly reception has remained neglected. This dissertation explores the use of the Tale of the Heike by seventeenth-century scholars of “military studies” (heigaku or hyōgaku), who compiled treatises and commentaries (gunsho) on leadership, statecraft, history, and ethics aimed at domain lords and warrior-officials of different levels. This study focuses on the category of evaluative commentaries (hyōban) on medieval texts that combined critical discussion, admonition of rulers, and plausible “secrets” in order to caution against mistakes and explain proper leadership. I argue that the commentary Heike monogatari hyōban hidenshō (1650) reinterpreted the courtly and Buddhist content of the Tale of the Heike in terms of pragmatic leadership and ethics relevant to warrior-officials of the Edo period (1603-1868), and that this commentarial appropriation brought the Tale of the Heike into the sphere of warrior-officials’ scholarship and cultivation. The dissertation begins with a detailed overview of the understudied field of military studies in premodern East Asia and Japan. Based on an analysis of primary sources, I then discuss the content and commentarial approaches of evaluative commentary on the Tale of the Heike, its readership and circulation, as well as related texts. The study concludes with a comparative analysis which situates the commentary within the Japanese discourse of historical discussion and admonition, and also places it in the category of didactic guides to statecraft that are found in different cultures and are known as “mirrors for princes.” This study reveals a new facet of the Tale of the Heike’s reception centered on didactic commentarial works influenced by military studies, which constituted an important current in premodern Japanese intellectual history that shaped perceptions of state, society, leadership, and identity of warrior-officials throughout the Edo period.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".