Bibliographic record
Abstract
Over the past ten years, the publishing and book selling industries (in Canada and elsewhere) have undergone a process of hyper-concentration that seems to threaten the future of independent publishing. How might this changing environment reflect on the attitudes of independent publishers toward translation and on the way they handle translation projects? This is the question this article seeks to examine. It is based on the first case study of a research programme that consists in following, by use of an ethnographic approach, the production process of literary translations in three independent Montréal-based publishing houses: from negotiations over the acquisition of translation rights to the launch of the translation. The article is divided into three parts. The first explains the rationale, methodology and ethics underlying this research; the second part tells the story of the title under study in a way that highlights the range of actors involved in the production of this translation, their own constraints and concerns, as well as the way publishing, editorial and linguistic/stylistic decisions intertwine. Based on this particular case, the third part discusses some of the strategies a publisher and his collaborators may devise in order to produce literary translations in an independent but network-based, competitive way. Particular emphasis is placed on strategies of cooperation such as co-translation and co-edition publishing, as well as on the role played by literary agents in the allocation of translation rights.
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 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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.020 | 0.033 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".