Survivances: Translating Cultural Memory in Quebec
Bibliographic record
Abstract
My research integrates three fields—translation studies, memory studies and Quebec studies—to study translation and cultural memory in the Quebec context. Focusing on intersections of national, migrant and Indigenous memory, it seeks to elucidate the role that translation plays in the construction and circulation of cultural memory across languages, cultures and affiliations. It proposes three angles of analysis, each of which focuses on a different facet of memory and translation. The first, TRANSLATION AS REWRITING, examines Michèle Lalonde’s 1968 Quiet-Revolution-era poem “Speak White,” situating it as a site of national memory and identity and tracing its afterlives in intra- and interlingual translations and adaptations. The second, TRANSLATION AS COUNTER-MEMORY, focuses on the 1970s counter-cultural periodical Mainmise to examine collective memory as a translational phenomenon based on re-identification and retemporalization—the construction of alternative collective references through cultural borrowing and transfer. The third, TRANSLATION AS RECLAMATION, explores the role that translators play in reclaiming and transmitting cultural memory through different forms of linguistic and cultural (self) translation, focusing on the works and trajectories of Cree-Algonquin writer Bernard Assiniwi and Innu poet Joséphine Bacon. Issues surrounding language, memory and identity have been abundantly explored in Quebec from historical, sociological and literary perspectives. This thesis approaches these questions from a slightly different angle by shifting the focus squarely onto translation as both a vehicle of memory and memory process in itself. Any discussion of translation and memory inevitably evokes notions of fidelity to or affinity with an originating source, be it a symbol, text or artefact, a story, performance or event, a place, individual or community. Confronted with change, alterity or trauma, these sources can be undermined, assimilated or even erased, but they can also be renewed, transposed and liberated into new forms, giving rise to different resolutions along a continuum of similarity and difference, of continuity through transformation. Through an examination of key literary works and other sites of memory-encounter and cultural production, this thesis sheds light on moments or instances of identitary crisis, rupture and unfolding that highlight the translational, emergent nature of meaning, memory and identity and foreground the diverse and complex ways that their survivance is assured by the very act of their transformation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".