Reference and Representation in Translation: a Look Into the Translator's Resources
Bibliographic record
Abstract
This study investigates the effect of schematic knowledge on the appropriateness and communicative acceptability of the translation rendered of four ambiguous contextless texts. The four texts (a road sign and three advertisements) were translated in two separate sessions by twenty-eight students pursuing a B.A. in English language and literature. In the first session, the students were provided with the texts decontextualized: while in the second they were provided with the same texts in the contexts they usually occur in. In the two sessions the students were asked to explain in a separate sheet why thev translated each text in the way they did. Two notions, closely related to the translating process, are discussed in the analysis of the translation provided. These notions are "reference" and "representation". The analysis has shown that the student translators resorted to referential strategies in the process of translating when they were aware of the relevant contextual dimensions of the target text. Their translations in this case retained the registral, rhetorical, and formal characteristics of the types of texts they translated. The analysis has also shown that when unaware of the pertinent contextual dimensions of the text, the student translators resorted to representational (introspective) strategies whereby contexts and world realities deriving from experiences and worlds other than those intended by the SL text producer were created, and the translations bore rhetorical, registral, and syntactic features relevant to the contexts and world realities the translators created.
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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 | 0.021 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".