Translator Identity and the Development of Multilingual Resources for Language Learning
Bibliographic record
Abstract
ABSTRACT Although a range of studies suggest that translation is a valuable pedagogical tool for language learning, the process of translation has not been adequately investigated. Further, the identity of the translator is invisible in much research on translation. To address these gaps in the field, the author draws on a self‐study of her translation of English books into Bangla and uses narrative inquiry methods to investigate the process of translation and the negotiation of identity. Drawing its theoretical underpinnings from Norton’s work on identity (Darvin & Norton, 2015; Norton, 2013), the author investigates how she navigated her translator identity with respect to investment, capital, and ideology. Extending Nida’s formal and functional equivalence in translation (Nida & de Waard, 1986), the author develops a “continuum of equivalence” model to facilitate decision‐making in the translation process and illustrates how she used this model in her English‐Bangla translations. Drawing on her study, the author makes the case that the model could enhance the use of translation as a pedagogical tool to promote language learners’ critical language awareness, multicultural knowledge, and creative thinking. Further, her study makes visible the identity of the translator, an important stakeholder in the promotion of multilingualism in language education internationally.
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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 | 0.026 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".