Bibliographic record
Abstract
http://dx.doi.org/10.5007/2175-7968.2016v36nesp1p177As language referential data banks, corpora are instrumental in the exploration of translation solutions in bilingual parallel texts or conventional usages of source or target language in monolingual general or specialized texts. These roles are firmly rooted in translation processes, from analysis and interpretation of source text to searching for an acceptable equivalent and integrating it into the production of the target text. Provided the creative and not the conservative way be taken, validation or adaptation of target text in accordance with conventional usages in the target language also benefits from corpora. Translation teaching is not exploiting this way of translating that is common practice in the professional translation markets around the world. Instead of showing what corpus tools can do to translation teaching, we start our analysis with a common issue within translation teaching and show how corpus data can help to resolve it in learning activities in translation courses. We suggest a corpus-driven model for the interpretation of ‘business’ as a term and as an item in complex terms based on source text pattern analysis. This methodology will make it possible for teachers to explain and justify interpretation rules that have been defined theoretically from corpus data. It will also help teachers to conceive and non-subjectively assess practical activities designed for learners of translation. Corpus data selected for the examples of rule-based interpretations provided in this paper have been compiled in a corpus-driven study (Poirier, 2015) on the translation of the noun ‘business’ in the field of specialized translation in business, economics, and finance from English to French. The corpus methodology and rule-based interpretation of senses can be generalized and applied in the definition of interpretation rules for other language pairs and other specialized simple and complex terms. These works will encourage the matching of translation study theories and corpus translation studies with professional practices. It will also encourage the matching of translation studies and corpus translation studies with source and target language usages and with textual correlations between source language real usages and target language translation real practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".