Bibliographic record
Abstract
This article will focus on communities which translate and communities which are translated, with an emphasis on the often unintended, unexpected, and unwanted effects of translation.
 
 Beginning with the scepticism – ‘hostility’ would perhaps be a better word – shown by Augustine towards Jerome’s undertaking to produce a new Latin translation of the Old Testament based on the Hebrew text rather than the Greek version of the Septuagint, and from there moving on to Mark Fettes’s discussion (in In Translation) of the reception of the translation into English of Haida myths by the Canadian poet Robert Bringhurst, as well as to the translation, also into English, of literary texts in Oriya, one of the national languages of India, I will draw attention to what, in these cases at least, has been perceived by some – usually those left out of the process of translation – as the danger or violence of translation.
 
 Given such a negative perception of translation, generalized in the Italian adage traduttore traditore, the question arises as to how this translation effect can at the very least be reduced, if not eliminated entirely, and how the “community with foreign cultures” that Lawrence Venuti writes of in “Translation, Community, Utopia” can come into being. A collaborative approach to translation involving participants from both source and target, foreign and domestic cultures – a new community of translators – will be put forward as a possible solution
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 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".