Investigating Translation Competence: Conceptual and Methodological Issues
Bibliographic record
Abstract
The PACTE group uses empirical-experimental research methods to investigate translation competence and how it is acquired. This article presents the design of our Translation Competence research project and the first results of a pilot test carried out to evaluate our research design and the instruments that will be used in the experiment. The first results of the pilot test are related to decision-taking in the translation process and the interrelation between the use of internal and external support. The results point to differences in the processes followed by professional translators and other language professionals. Furthermore, the pilot test has proved the reliability of our instruments.
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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 | 0.541 | 0.723 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.022 | 0.025 |
| Science and technology studies | 0.007 | 0.047 |
| Scholarly communication | 0.024 | 0.037 |
| Open science | 0.010 | 0.017 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".