Translating Cultures: A Linguistic Reading of A Dream of Red Mansions1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper attempts to present a contrastive analysis of sets of translation of the same work, in the same language but of different versions, from a linguistic perspective, by means of Motion event. In analyzing the same, we attach great importance to the source text effect as well as the translator effect on the target text. As suggested by Talmy (1985, 2000), different languages contain different systems of verbs of motion, which could be described by a universal pattern of its semantic elements: Figure, Ground, Motion, Path and Manner. Accordingly English and Chinese could be both classified as satellite-framed languages (the other alternative is verb-framed languages) (Talmy 2000). And in this wake, many researches flourish in different language contexts in hopes of validating the “linguistic relativity” hypothesis. However, up to now, a linguistic adoption of Motion event has not been found in analyzing the possible source and translator effects underlying the translation works in the same language but of different versions. In this view, this paper tends to utilize this semantic category to analyze the above-mentioned effects as represented in David Hawkes, John Minford, and Xianyi Yang and Galdys Yang, in the light of their respective translatings of A Dream of Red Mansions (or the Story of the Stone) .
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 it