Trilingual Literary Self-Translation: An Interview with Two Montreal Writers, Hugh Hazelton and Alejandro Saravia
Bibliographic record
Abstract
Abstract In addition to being multilingual writers who have made their home in Montreal, Hugh Hazelton and Alejandro Saravia have both chosen to translate a certain number of their own writings and—unlike most self-translators—to incorporate three languages (English, French and Spanish) into their practice. This interview explores a number of questions related to Hazelton’s and Saravia’s experiences with trilingual self-translation. In responding to these questions, the writers also reflect on key people, books and circumstances that influenced their linguistic and cultural attachments; ways that language, place and identity intersect; perceived distinctions and similarities between authoring, self-translation and allograph translation; and obstacles facing the contemporary publishing industry, within Canada and beyond.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.045 | 0.019 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".