Beyond Translation Proper—Extending the Field of Translation Studies
Bibliographic record
Abstract
Modern society demands many different kinds of translation or translation-like activities which often exceed the boundaries of what translation theory traditionally terms translation proper. Highly functional translations, localisation, précis-writing, expert-to-layman communication, etc. are all part of modern life, but where do such activities fit in theoretically? In this article I shall discuss the fact that despite Jakobson’s classical definition, intralingual translation or rewording is de facto peripheral to translation studies and I shall argue that the relationship between interlingual and intralingual translation is a neglected area of research, as is a thorough description of intralingual translation. Since Jakobson’s definition, general definitions of translation have become less inclusive. This I consider a major setback as there seems to be much to gain theoretically as well as practically by looking for similarities and differences between various kinds of translational activities. With the ulterior motive of putting intralingual translation (back?) on the map of translation studies and to encourage future empirical research within this area I shall argue for a broader perception of translation and consequently of translation studies as a discipline. Inspired by Jakobson (1959), Toury (1995) and Tymoczko (1998, 2005), I shall attempt to draw up an open definition of translation which reflects the many-faceted nature of the phenomenon.
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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 | 0.049 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.008 | 0.064 |
| Scholarly communication | 0.020 | 0.044 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".