Bibliographic record
Abstract
The Wakan roeishU (fO~AAIDl<~.ca.1012) 1 embodies a key moment in the process of assimilating Chinese poetry into the Japanese poetic tradition.It crystallized a canon of Chinese poetry specifically for Heian Japan that exerted a far reaching influence.For example, a line of Chinese poetry from the Wakan roeishU even surfaces in Kawabata Yasunari's acceptance speech for the Nobel prize in 1968.Kawabata cites the Japanese art historian •Yashiro Yukio who states that "one of the special characteristics of Japanese art can be summed up in a single poetic sentence: 'The time of the snows, of the moon, of the blossoms-then more than ever we think of our comrades."'2 This "poetic sentence" (shigo [WJ~B]) is actually an adaptation 3 of one line from a couplet by Bai Juyi (S.@~, 772-846), no.734 of the Wakan roeishu, "?JfWJ~~-Wifm:ft, ~ !H~IWJ jjiti:{!" ("Lute, poetry, wine-these friends all have abandoned me, Snow, moon, blossoms-these times, more than ever I think of you").This is the locus classicus for setsugekka, now a fixed expression in Japanese for the seasonal beauties of nature, but actually three characters from one line of one couplet from Bai Juyi's poem, "Sent to Chief Musician Yin.'"'The Wakan roeishu itself has 1 For the English translation see J.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".