‘Translation Archaeology’ in Practice: Researching the History of Buddhist Translation in Tibet
Bibliographic record
Abstract
This paper is a report on a 10-month period of archival research aimed at uncovering key data related to the translation of the Indian Buddhist canon into Tibetan, a remarkable achievement that took some 900 years to complete. Our previous research relying on secondary (English-language) sources found that much information was either missing or unsubstantiated. In particular, the seemingly simple question of how many translators were involved in producing the Tibetan canon could not be satisfactorily answered. Without this foundational data, it is impossible to determine how many texts each translator produced, or who the most prolific translators were in Tibet’s history. Thus, Phase 1 of the archival research was to record the names, dates, and other relevant data of all the translators listed in the Tibetan canon. Phase 2 focused on researching biographical materials of some of the translators discovered during Phase 1. Pym calls this type of work “translation archaeology,” which is concerned with answering questions such as “who translated what, how, where, when, for whom and with what effect” (Pym 1998: 5). The data gathered at the end of the research period is presented and analyzed, difficulties encountered are discussed, and areas of further research are suggested.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".