Competence and Creativity in Translation: Multilingual Perspectives
Bibliographic record
Abstract
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.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".