Translating Tea: On the Semiotics of Interlingual Practice in the Hong Kong Museum of Tea Ware
Bibliographic record
Abstract
This paper explores the nature of interlingual translation practice in the museum, focusing in particular on the ways in which visual elements shape or constrain the translation of verbal texts. The museum represents a particularly complex semiotic environment in which various systems of signification (verbal, visual, spatial) interact to produce meaning in a way that is said to be “combinatorial and relational.” Such interactions take place both at intra-semiotic levels (e.g. between objects, between objects and photographs, or between texts) and inter-semiotic levels (between these various verbal and visual elements). Interlingual translation must negotiate such multiple polarities if an effective target text is to be produced. The paper focuses on the Museum of Tea Ware in Hong Kong, to examine how various semiotic constraints affect the production of English target texts from their Chinese source texts. One such constraint is that of spatial aesthetics, the way in which text is positioned in relation to visually salient pictures or objects in a given ensemble. A second constraint is the generic nature of the text as defined by its position in the museum text-hierarchy. Thirdly, the paper argues at greater length that the nature of modification found in the target text is directly proportional to whether the relation between given verbal and visual elements is either paradigmatic or syntagmatic.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".