PROGNOSTICATION IN CHINESE BUDDHIST HISTORICAL TEXTS THE GĀOSĒNG ZHUÀN AND THE XÙ GĀOSĒNG ZHUÀN
Bibliographic record
Abstract
This paper explores topics and techniques of prognostication as recorded in medieval Buddhist historical literature, with an emphasis on theGāosēng zhuàn高僧傳 (GSZ) andXù gāosēng zhuàn續高僧傳 (XGSZ). The paper first provides a short survey of how prognostication is treated in Chinese Buddhist translated texts. In these ‘canonical’ sources there is clear ambiguity over the use of supernatural powers: on the one hand, such practices are criticised as non-Buddhist or even heterodox; on the other, narratives on Śākyamuni’s former and present lives as well as accounts of other buddhas, bodhisattvas, and the Buddha’s disciples abound with descriptions of their special abilities, including knowledge of the future. In contrast, the GSZ and XGSZ display a clear standpoint concerning mantic practices and include them as integral aspects of monastics’ lives. The two texts articulate that the ability to predict the future and other supernatural powers are natural by-products of spiritual progress in the Buddhist context. This paper discusses the incorporation of various aspects of the Indian and Chinese traditions in monastics’ biographies, and investigates the inclusion of revelations of future events (for example, in dreams) and mantic techniques in these texts. In addition, it traces parallels to developments in non-Buddhist literature and outlines some significant differences between the GSZ and the XGSZ.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".