Bibliographic record
Abstract
This article presents an overview of contemporary bibliomigrancy patterns of translated fiction from the province of Quebec to Sweden, between 2000 and 2020. Quebec and Sweden offer an interesting comparison, since French is considered a central language but the province of Quebec occupies a peripheral position in comparison with its Anglophone neighbours, whereas Swedish is considered a semi-peripheral language but Sweden occupies a central position in the Scandinavian subsystem. Drawing on theories on bibliomigrancy and polysystem, the article investigates 26 titles from the point of view of external translation history, focusing on the following questions: What was translated? When was it translated? Where was it translated? Who translated it? Why was it translated? The analysis shows that different genres, notably novels, picture books, and graphic novels, have been translated into Swedish during the investigated time frame, with different patterns regarding factors such as publication interval, translators, and translation subsidies. The increasing tendency of Quebecois titles appearing in Swedish follows the increasing trend of French as a source language in Sweden’s literary market, in contrast to the more even pace of translated literature into Swedish more generally. The results further suggest that a region’s language may have a more significant influence than its geopolitical position in the international market of translations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.114 | 0.033 |
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".