GG and the City: What Canada’s Major Translation Award Reveals about Its Metropolises
Bibliographic record
Abstract
In this article I consider the predominance of metropolises in the Canadian publishing sector through the lens of award-winning translations. My research is based on a database of information on Canadian literary prizes (gathered through reports and websites of Canadian prize-granting organisations and Aurora, the Library and Archives Canada catalogue which also queries the WorldCat search engine), specifically on the awardees and finalists of the Governor General’s Literary Awards (GG) in both Translation categories (English-to-French and French-to-English). Data about the cities and publishers connected with the original books and the translations, as well as about the finalist translators’ places of residence, demonstrate that Montréal occupies a central place in this landscape. Indeed, an overwhelming majority (82%) of the finalists in the GG English-to-French Translation category were published in this city; likewise, an appreciable number of books translated from French into English (21% of the finalists in this category, versus 54% for Toronto and 16% for Vancouver). While it is hardly surprising that Toronto and Montréal are, respectively, home to most English and French original publications, Montréal stands out as the main city where translators, in both language combinations, live and work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".