Effects of Specific Training on the Ability to Deal with Cultural References in Translation*
Bibliographic record
Abstract
The aim of this empirical study (carried out as part of a wider research project – see “Credits” below) was to discover the effects of specifically designed pre-service translator training on the trainees’ ability to deal with cultural references, a text segment type which is widely considered as potentially problematic for the translator. 1 Specifically, we set out to discover any significant differences, as a result of said training, in trainees’ ability to: (a) detect cultural references within a text, (b) provide multiple feasible options (variants) to translate them, (c) evaluate those potential options, and (d) apply reasoning in making a final choice from the options. The rationale and nature of the specific training involved has already been extensively reported in González Davies and Scott-Tennent (2005). In the present article, we focus our attention on reporting and discussing its observed effects. The design of the specific training drew heavily on a previous study on specific translator training in problem-solving, reported in Scott-Tennent et al. (2000) and González Davies et al. (2001).
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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 | 0.006 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".