Bibliographic record
Abstract
The major works of Japanese historical writing have been translated into English.Believing that these translations are made for the use of scholars, I have cited them freely; however, I have preferred my own renderings of some passages, as indicated in the footnotes.Japanese names are given in Japanese style; that is, family name followed by personal name.For example, the name of Kitabatake Chikafusa gives first his family name, Kitabatake, and then his personal name, Chikafusa.The particle "no" has been eliminated from most names as unhelpful to readers of English (e.g., Sugawara no Michizane means "Michizane of the Sugawara family"; this becomes Sugawara Michizane, in the modern Japanese style).The only exceptions are the names of the men associated with the composition of Kojiki, Hieda no Are and O no Yasumaro, which are so familiar that they would seem strange without the particle.The distinction between long and short vowels is preserved throughout the work by the customary macrons over "O" and "u" to indicate long vowels, except for the names of Tokyo, Kyoto, and Osaka, and the terms Shinto and Shogun which are familiar in English without macrons.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.214 | 0.064 |
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".