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Record W2739016542 · doi:10.7202/1040465ar

Translation in the European Parliament: The Study of the Ideational Function in Technical Texts (EN/FR/ES)

2017· article· en· W2739016542 on OpenAlexvenueno aff
María Azahara Veroz

Bibliographic record

VenueMeta Journal des traducteurs · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics and Discourse Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsParliamentPublicationLinguisticsFunction (biology)BeneficiarySystemic functional grammarSentenceProcess (computing)GrammarFrame (networking)CognitionPolitical scienceComputer sciencePsychologySociologyLawPoliticsPhilosophy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.086
GPT teacher head0.296
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2017
Admission routes1
Has abstractyes

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