Tenacious technophobes or nascent technophiles? A survey of the technological practices and needs of literary translators
Bibliographic record
Abstract
In a context of increasing investigation of technology use by translators of pragmatic texts, there appears to be an assumption that literary translation is a unique practice and that digital tools designed to improve the productivity of non-literary translators have few applications in the literary domain. The present study seeks to challenge that assumption and find out what tools and resources literary translators actually employ in practice; how they interact with source and target texts, manage terminology, and conduct linguistic research; and what their needs may be for training in this area. Members of the Literary Translators’ Association of Canada were invited to complete an anonymous self-administered online questionnaire on their use of technology and digital resources. Results indicate that literary translators make extensive use of standard tools and electronic resources but little use of more specialized technology. However, it was also found that some respondents make ‘creative’ use of specialized technology and that literary translators have a broad range of needs, particularly for linguistic and cultural research, leading to a recommendation that future investigation in this area focus on the improvement of digital tools and resources to support literary translators in meeting their ad hoc needs.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".