Translating Italian-Canadian Migrant Writing to Italian: a Discourse Around the Return to the Motherland/Tongue
Bibliographic record
Abstract
A two-way bond between translation and migration has appeared in the most recent texts in the social sciences and humanities: this connection between the two is exemplified by the mobility metaphor, which considers both practices as journeys across cultural, linguistic and geographical borders. Among the different ways this mobility metaphor can be studied, two particular areas of investigation are of interest for this research: firstly, migrant writing, a literary genre shaped from the increasing migratory movements worldwide; the second area of interest is literary translation, the activity that shapes the way these narratives are disseminated beyond the linguistic borders they were produced in. My investigation into the role of literary translation in the construction and circulation of a migrant discourse starts with the claim that writing and translation in itinerant contexts are driven by, and participate in, the idea of the journey: an interlingual and intercultural flow regulated by social/economic/artistic constraints, a movement in which the migrant experience is ‘translated’ in writing and then ‘migrated’ across languages and spaces. The present analysis focuses on the representative case study of migrant narratives by Canadian writers of Italian descent: their shared reflections on the themes of nostalgia and the mythical search for roots, together with a set of specific linguistic devices – hybridity, juxtaposition of languages, idiolects and registers – create a distinctive literary migrant discourse, that of the return to the land of origin. Guided primarily by the theoretical framework of Cultural Studies, the first part of this work seeks to illustrate how thematic and linguistic elements contribute to the construction of a homecoming discourse in original migrant narratives, and how this relates to the translation practice. Subsequently, the analysis moves to the examination of how these motives are reproduced in the translated texts, and what is/are the key rationale/s behind the translation of this type of works. Ultimately, my research takes a sociologically informed interest in the influence of translation and its agents in endorsing and/or manipulating this rationale in the receiving culture. In fact, this research aims to represent equally the human and cultural-linguistic aspects that affect these translational journeys, concentrating, firstly, on the actors (authors and literary translators) and the social and artistic environments that surround the production of both the source and target texts and, subsequently, on the texts themselves.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.032 | 0.029 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".