О сборнике «Гирлянда рассказов о прошлых жизнях Учителя» (по материалам ксилографического издания монастыря Далуншань)
Bibliographic record
Abstract
Introduction. The Jātakamālā by Āryaśūra (4th c. AD) had been very popular in India, and was considered an early example of a structurally particular poetic composition. The Third Karmapa Rangjung Dorje (1284–1339) supplemented Āryaśūra’s 34 jātakas with 67 more stories of his own. Rangjung Dorje holds a prominent place both in the history of his lineage and in the history of Tibetan culture at large, his legacy remaining of immense interest to date. So, he authored a number of famous texts, such as Aspiration of Mahamudra (Tib. phyag chen smon lam), The Profound Inner Principles (zab mo nang don), and a collection of jātakas — The Garland of Birth Stories of the Teacher (Buddha). Goals. The article aims at analyzing the text of the specified collection available at the Buddhist Digital Resource Center (BDRC) to introduce data on the former and its structure. Results. The Garland of Birth Stories of the Teacher includes 101 jātakas — 34 ones by Āryaśūra and 67 ones by Rangjung Dorje. The last, 101st, story tells about the life of Shakyamuni Buddha. The paper deals with the 1430 Beijing xylographic edition of Dalongshan Monastery (Tib. ta’i lung shen).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".