Bibliographic record
Abstract
A competent translator seems to make extensive use of inferential strategies in the process of understanding a source text in translation. It also appears that extralinguistic knowledge plays an important role in this inference. However, it has not been determined how important it is relative to other components of translator competence and in what way it is important. This study investigated the use of extralinguistic knowledge in comprehension processes by analyzing data from L2->L1 translation, a questionnaire and a think-aloud study. Professional translators, translation students (or semi-professionals), and language learners participated in the study. Major (albeit tentative) findings are the following: (i) The use of extralinguistic knowledge that is most relevant for a specific translation problem makes the inferential process more concise and efficient and it leads to a higher-quality translation; (ii) “Translation effort” can compensate for overall lack of linguistic and extralinguistic competence in translation. It is suggested that a translator needs to have a broad base of specialized knowledge as well as world knowledge. Also, s/he should bear in mind that the greater the effort or involvement with the translation, the better the output.
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 | 0.013 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".