Les quatrièmes de couverture comme lieu d’inscription d’une représentation de la littérature traduite : romans canadiens d’expression française en traduction polonaise (2000-2016)
Bibliographic record
Abstract
The Back Cover as a Place for Creating an Image of Translated Literature: Polish Translations of French-Canadian Novels (2000-2016) The back cover of a book contains peritext added by the publisher, with a double function of information (about the author and the work) and invitation to read the book. That is why it also becomes the place where publishers decide on a certain image of the books. For this study, we have collected back cover texts from French-Canadian novels which were published in the Polish translation in the years 2000-2016, and we have considered them as a certain image of this literature given to the Polish reader by the publishers. These texts are also a source of information about this literature for the readers. The results of the analysis of the covers of 27 novels published in Poland in the studied period allow us to state that this image is deformed and simplified: it does not reflect the language and regional differences of Canadian literature today. The works themselves belong to such genres as fantasy, thriller or chick lit: they are attractive, pleasant to read, often awarded and adapted for the screen. The “cover image” of the French-Canadian literature given by Polish translations reflects rather the strategy of their publishers: it seems that their choices of translated works are directed mainly by economic prudence.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".