Bibliographic record
Abstract
There has recently been an upsurge of articles about 'intralingual translation': producing a version of a medical document that will be suitable for a lay readership or updating the language of classic literary texts. It is argued here that updating and dialect rewording are really instances of interlingual work, while preparing plain-language derived texts for lay readerships is so different from interlingual work that the word ‘translation’ should not be used. Such intralingual work differs procedurally, formally and in particular functionally from interlingual work. The main function of those preparing such texts is to explain or to make a text more readable, whereas most translators spend most of their time engaged in ‘equivalencing’: producing a target-language wording which they think means more or less the same as the corresponding passage in the source text. Translating is best seen as invariance-oriented work between languages, with non-equivalencing activities playing a minor though important role. Intralingual work on the contrary is varianceoriented; such work would be pointless if the aim was to preserve the style and the detailed denotative meaning of the source. Among the topics discussed are paraphrasing, editing, respeaking, repeating and intralingual reported discourse. The article concludes with a discussion of why the title question matters.
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 | 0.032 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.090 |
| Scholarly communication | 0.020 | 0.041 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".