Translations not in the making? Rejections, disruptions and impasses in translator–publisher correspondence
Bibliographic record
Abstract
This is a study of a frequently occurring but seldom studied phenomenon: that of proposed translations, rejections and disruptions in translators’ work. The study is based on the correspondence of 64 translators in the archive of the Werner Söderström (WSOY) publishing house in Finland between the 1880s and the 1940s. It deals with around 180 translator suggestions and an almost equal number of rejections discovered in translator–publisher correspondence. The research draws on actor-network theory and its focus is on emergent processes and controversies, exploring the events and decisions as they unfold in the archived interaction between translators and publishers. The large number of rejections, the various kinds of disrupted processes in the making of translations and the contingency of the translation event are set in high relief. Studying the way translations come into being – or do not come into being, as is often the case – shifts the focus from translations as products to the translation event: disruptions and impasses, it will be shown, constitute a significant share of translators’ and editors’ work and are crucial in understanding the translation process.
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 | 0.029 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".