Spanish Imposition: Literary Self-Translation Into and Out of Spanish in Canada (1971-2016)
Bibliographic record
Abstract
To date, region-based scholarship into Hispanophone literary self-translation overwhelmingly locates practices in spaces where Spanish not only has official language status but is also the dominant language. Yet, in officially bilingual (English-French) Canada, at least 25 people translated their own writing into or out of Spanish between 1971 and 2016, making these writers the single largest subset of Canada-based self-translators working with an unofficial language. But who are these authors? What might be said about their self-translations? And what does it mean to self-translate using Spanish when that language does not have official status? Adopting a product-oriented perspective, I explore these questions via three lines of enquiry: 1) time and space: when and where were these writers born? 2) frequency: how often do these authors self-translate? and 3) language: how can self-translations and self-translators be characterized in terms of language variety and combinations? Ultimately, I argue that, in the context of Canadian self-translation, the Spanish language is simultaneously imposing—pushing resolutely against paradigms of two-ness embodied by official bilingualism—and imposed upon, since it lacks official status of its own and the infrastructural robustness that accompanies it.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".