Bibliographic record
Abstract
In his recent critique of contemporary Translation Theory (TT), Singh (2005) argues that (1) as things stand, contemporary TT is not really a theory of translation but an exploration that seems to simply assume that the various uses, literal and metaphorical, of the word ‘translation’ and of the techniques employed in what languages normally refer to as translation delimit an interesting domain of which one can construct a theory and (2) one of the new ways in which translation and TT need to be conceptualized is to revisit and renew the old ways in which they used to be seen, albeit with a difference. This paper will, in effect, sketch out a possible itinerary for such a revisit. The purpose of this paper is, in other words, to summarize that critique and to sketch out the content of what we view as crucial courses for a programme in translation that could constitute the first proactive steps for recovering the baby contemporary TT seems to have thrown out with the bath water of structuralist ‘equivalence’. These courses have been and are being tried at the newly instituted graduate programme in translation at the University of Peradeniya in Srilanka.
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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.027 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.069 | 0.050 |
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".