Bibliographic record
Abstract
Text linguistics enables translators to [201c]climb up,[201d] to work more effectively from the level of text with [201c]textual judicial authority.[201d] It should enable them to [201c]look down[201d] as well at lower units as functional units. Technically, discussions of translation often treat localized passages rather than full texts. The notion of Unit of Translation (UT), once defined, is thus useful for bridging the technical gap between the full text and its components in describing relationships involved in a translation, and looking at a localized passage's potential accountability to the whole text. This article approaches the issue of UT from the point of view of division of labour between short-term and long-term memory in translating, and defines the UT functionally as textual unit instead of language unit which maintains its textual integrity by performing three functions, viz. syntatic bearer, information carrier, and stylistic marker. Text translation thus boils down to the preservation of the textual integrity of each UT not in syntactic form but in function, given the necessary rank-shifts in the process. To that end, it argues, the key functional UT can be set at the level of sentence. The article is a revised version of the first part of Zhu (1996a).
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 | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".