Beware the source text: five (re)translations of the same work, but from different source texts
Bibliographic record
Abstract
Source text (ST), although a central concept in translation studies, has remained vaguely defined. This complicates the identification of a translation’s ST, which in turn creates problems for research. Associating translations with the incorrect ST(s) leads to questionable conclusions and categorizations, especially when dealing with the types of translation that are defined and theorized with reference to their relationship with their ST(s), such as retranslation, indirect translation, pseudotranslation and self-translation. Our case study of five Finnish translations of Jules Verne’s Vingt mille lieues sous les mer demonstrates that these assumed retranslations have different STs. We adopt the notions of work and text to establish the relationships among the translations and STs involved: texts are representations of a work , and a work , in turn, is a literary creation implied by its various texts . Although the five Finnish translations have different source texts , they are all – as are their STs – texts of the same work . In other words, if source text is understood to be a text , the five translations are not, strictly speaking, retranslations; however, if source text is understood to be a work , then they are all retranslations of the same work . Therefore, the categorization of these translations – and thus also the points of view from which they can be studied – depends on whether source text is defined as a text or as a work.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".