Avaliando a re-expressão e criatividade de alunos de tradução
Bibliographic record
Abstract
Although translation may be considered a two- (or three) phase communication process, consisting of comprehension – (conceptualization) – re-expression, most theoretical and pedagogical studies have been devoted to comprehension and conceptualization. There is, however, an increasing need to establish a theoretical basis for the third phase since, contrary to Boileau’s dictum (that well conceived ideas can be easily expressed), even when comprehension is complete, words do not come easily. If re-expression is to be better taught, evaluation of re-expression must be better thought. This paper focuses on the evaluation of re-expression in translation. Based on an in-depth study of various English texts translated into French by some 38 first-year translation students, it first calls attention to the difference between expression and re-expression and between creativity and literality, viewing the former as a ‘deviation’ from the latter. Second, it argues in favour of positive evaluation, given that negative evaluation has a relatively limited impact on the learning process, and further study of it would not be very productive. Positive evaluation involves analysis of successful solutions rather than of errors. The paper goes on to analyze which aspects of re-expression need to be evaluated and how this should be accomplished.
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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".