Archetypes unbound: domestication of the five Chinese imperial consorts
Bibliographic record
Abstract
When Chinese legendary figures were translated into waka or wabun, it seems more often the case than not that a degree of autonomy from the original contexts was sought in order to enhance accessibility for and appeal to Japanese readers with varied levels of learning in Chinese.While honoring some of the attributes that had been encoded in Chinese archetypes, Japanese authors strove less to elaborate their socio-cultural contexts so as to reconfirm their alien status than to "naturalize" the already heavily codified cultural icons by finding or inventing Japanese vocabulary to relocate them in the framework of Japanese lyricism.Whereas in kanshi and kanbun the figures may be bound to the historical and ideological connotations, because of the importation of a larger part of, if not the entirety of, vocabulary and rhetoric, attributes of the archetypes were susceptible to freer modification in waka and wabun; poets and prose writers curtailed or censored some attributes while highlighting or adding others so that the archetypes might meet the protocols of Japanese poetry and, though less tightly defined, those of the narrative.In this process of the adaptation of Chinese characters, varied degrees of ahistoricization as well as aculturalization transpired.When we consider the conventional contrast between Chinese writing and Japanese writing, the oppositionals often paralleled respectively with the masculine md the feminine.The practice of translation of Chinese female characters into the bungo presents itself as a site of gender and ethnicity border-crossing: how did the females portrayed in the "masculine" language get translated into the "feminine" language?In this paper, I will focus on typical bungo renditions of the so-called "Five Consorts" ("gohi 1i~C.") made famous by the compositions of Bai Juyi (S.@~.772-846), Japan's favorite Chinese poet.The five-Wang Zhaojun
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".