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Record W780583598

Competence and Creativity in Translation: Multilingual Perspectives

2015· dissertation· en· W780583598 on OpenAlexfundaboutno aff
Carmen Ruschiensky

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2015
Typedissertation
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCreativitySociocultural evolutionMultilingualismCompetence (human resources)LinguisticsTranslation studiesQualitative researchSociologyPsychologyPedagogySocial psychology
DOInot available

Abstract

fetched live from OpenAlex

This thesis addresses competence and creativity in translation by focusing on the translator as a multilingual, historically grounded subject. Drawing on recent multilingualism research and integrating insights from translation studies, hermeneutics, sociolinguistics, and second-generation cognitive science, it is argued that translators do not simply transfer meaning between words, texts or cultures; they embody a relation to the languages and cultures they are translating, just as multilinguals do in code-switching, performing identities, and symbolically identifying with different linguistic and cultural meanings. To explore these ideas, I conducted a qualitative study on multilingual translation students in Montreal to learn more about their diverse backgrounds. Research results—covering a broad range of languages, age groups, life experience, education, and employment histories—suggest that the translation process cannot be defined without considering the sociocognitive complexity of translation and that translators, at every stage of their development, actively draw on their unique linguistic and sociocultural repertoires. A working definition of translators’ symbolic competence is proposed as a framework for analysing students’ interests in, attitudes about, and approaches to translating and for considering how translators, especially translation students, can potentially develop their competence and creatively in translation by exploring this nuanced terrain.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.613
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.090
GPT teacher head0.337
Teacher spread0.247 · 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.

Study designQualitative
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

Citations4
Published2015
Admission routes2
Has abstractyes

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