Tenacious technophobes or nascent technophiles? A survey of the technological practices and needs of literary translators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it