Redefining Translation Competence in an Electronic Age. In Defence of a Minimalist Approach
Bibliographic record
Abstract
Since the 1970s the notion of “translation competence” has been viewed as at least 1) a mode of bilingualism, open to linguistic analysis, 2) a question of market demands, given to extreme historical and social change, 3) a multicomponent competence, involving sets of skills that are linguistic, cultural, technological and professional, and 4) a “supercompetence” that would somehow stand above the rest. The general trend among theorists has been to expand the multicomponent model so as to bring new skills and proficiencies into the field of translator training. This trend may be expected to continue with the increasing use of electronic tools. Here it is argued, however, that the multicomponential expansions of competence are partly grounded in institutional interests and are conceptually flawed in that they will always be one or two steps behind market demands. On the other hand, a simple minimalist concept of translation competence, based on the production then elimination of alternatives, can help orient translator training in times of rapid technological and professional change.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".