Foreign Is Not Unfamiliar. A Translation Impact Study Involving Taiwan Subjects
Bibliographic record
Abstract
The research discussed in this paper is the last of a trilogy investigating the impact of foreignized and domesticated translations on sampled readers. The two precursors had been conducted in two Chinese Mainland cities (Beijing and Guangzhou) and this last one was conducted in the Taiwanese city of Douliou. Therefore, comparisons can be made with the two precursors in the discussion of the research procedure and findings of the Taiwan project. The paper begins with a brief introduction of the background in which the Taiwan project was conceived and a brief profile of the subjects sampled for the project. Then there is a description of the research design, including the hypotheses and field work methodology. Four hypotheses were tested: a) the subjects would have distinguishable ideas about the two renditions; b) they would have a more intense feeling of exoticism when reading the foreignized rendition; c) their degree of sensing the rendition to be exotic or not would be correlated with their other socio-linguistic perceptions of that text; d) the findings of the present investigation would corroborate those obtained from the two precursors. The description of the research design is followed by a presentation of the data analysis conducted for the project together with a statement of the conventions adopted for reading the statistics. Based on the data analysis, interpretations are made in relation to the hypotheses.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".