Bibliographic record
Abstract
An attempt is made to explore how Chinese character riddles are embedded into the English text by Ezra Pound. In the Chinese-speaking context, the character riddle is a kind of cultural practice with a problem and a solution as its two major components, which in turn basically correspond to the lexicogrammatical description of a character and the character under description. The rule is not abstruse: Players of such a language game are supposed to get at the solution on a given problematic basis. Just as one tends to construe experience through language, so does Pound construe his own experience of the character through the English language. His representation of such experience is evident throughout his translation of the Chinese classics, e.g., Confucian Analects. He describes the character in terms of the English lexicogrammar and then adapts the description into the English text with necessary configurations. In this sense, Pound is not only a translator but also a transmitter who stealthily introduces the character riddle into the English text, since every reader of his translation works has to come into play. He leaves the legacy of the riddle problem for his English readers to find their way out to guess the solution.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".