Bibliographic record
Abstract
This article argues that interpreters are crucial figures in the recording of history. Evidence taken from historical texts in ancient China is used to verify the claim that interpreters’ notes might have been used as a reference in composing historical records. By documenting the Tang dynasty (AD 618-907) policy to have interpreters interview foreign envoys and submit the relevant accounts to the Bureau of Historiography, this article provides background for the link between interpreters’ interview notes and history compilation in China. Evidence is further drawn from the history of the Sui dynasty (AD 581-618), whereby an interpreter’s mediated account of the emperor’s conversation with a Japanese envoy was directly adapted. Most interestingly, pictorial and written documents of foreign peoples made in the mid-6th century during the Liang dynasty (AD 502-557) were found to be very similar to the written accounts about these foreign peoples in Liangshu, the history of the Liang dynasty, completed in the early 7th century. Apparently, there is a solid link between the interview accounts and historical accounts about foreign peoples in China. Thus, there is a strong possibility that interpreters’ notes, in the form of reports, provide important, if not primary, sources for history compilation in China.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".