Translation in the European Parliament: The Study of the Ideational Function in Technical Texts (EN/FR/ES)
Bibliographic record
Abstract
In this paper we tackle the study of European Parliament technical texts in three languages, namely English-Spanish-French, focusing on their discursive features and, more concretely, on the way in which the ideational function is expressed in them. To achieve this end, we have followed the frame of the Systemic Functional Grammar compiling a trilingual parallel corpus composed of technical texts downloaded from the European Parliament (EP) Website. In accordance with the analysis proposed it is proved that these texts are characterized by a predominance of material processes, in particular, actions linked to the legal, administrative and economic world like adopt, approve, modify, create, transmit, publish, establish and sign, with their respective equivalents in Spanish and French. Although there are certain processes – mental (cognition) and verbal ones – that could have a mixed nature, as we have observed that equivalents are exchanged with each other in the different linguistic versions studied. Regarding the participants (agent, affected, recipient, beneficiary and sayer), most of them are institutions and acts/documents, when these participants in these processes are usually humans. We conclude that knowledge of these features could be useful for EU translators and should be used in the training of future translators as a guide in the translation process.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".