Parallel Corpora and ST Analysis: EU Regulations on Immigration in the Specialised Translation Classroom
Bibliographic record
Abstract
Starting from the assumption that “if corpora are to play a role in the translation professions of tomorrow, it is important that they impact on the education of the students of today” (Bernardini & Castagnoli, 2008, p. 40), this study endeavours to show how translation corpora of parallel texts (in English and in Italian) can be used in a Specialised Translation Master’s degree classroom. The point of departure is to examine parallel or aligned texts (originals and their translations) taken from the various European Union websites available for citizens to read and consult. The corpora currently being gathered include a variety of text typologies ranging from legal documents (such as regulations or directives), to administrative documents (such as White and Green papers) or informative texts (such as leaflets, brochures or web texts) (Felici, 2010, p. 101), all of them dealing with migration and asylum issues. The various types of documents are analysed quantitatively and qualitatively in class according to the following three main methodologies: Corpus Linguistics (Stubbs, 1996), Genre Analysis (Swales, 1990; Bhatia, 1993) and Systemic Functional Linguistics (Halliday, 1985; Halliday & Matthiessen, 2004). Classroom experiences in the past few years have shown that building ad hoc corpora (or do-it-yourself corpora as coined by Krüger, 2012, p. 514) for classroom consumption is a valuable and precious learning tool which enables students to hone their practical skills in the resource gathering process and consequently in the translation process itself. This study focuses on an EU regulation and shows how a lesson in class can be conducted with students at Cagliari University, Italy. The aim is to get students to work on the quantitative aspects along with the more qualitative linguistic elements of the ST (Source Text) in order to obtain greater awareness and understanding of professional translator strategies used by the professionals in the translation agencies of European Union institutions. From an educational and academic point of view, the linguistic and contrastive analysis of certain features of the ST along with the investigation of specialised terminology associated with contexts of migration and asylum and their equivalents in the TT (Target Text), have so far provided the Master’s students at Cagliari University with useful insights and sound knowledge of the linguistic characteristics of legal and institutional discourse in both English and Italian.
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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 | 0.022 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".