A Thing-bound Approach to the Practice and Teaching of Technical Translation
Bibliographic record
Abstract
The raison d'être of the technical text is in the access it affords to its referents. It follows that technical translation involves reverbalizing these referents rather than mapping semiotic structures from source-to target-language, as is the case with the other, more "hybrid" forms of discourse, in which functions of language other than the purely referential play a role. The source-language formulation is thus relatively unimportant. In the extreme case, which is not necessarily that of a poorly written source text, it can even be bypassed altogether, the translator drawing his information from the non-linguistic segments of the source text (equations, diagrams and the like) and verbalizing it directly in the target language.For Ivan Hirst, from whom I learned most of what I know about technical translation. The technical translator's stock in trade is an in-depth understanding of the referent. The following article proposes a number of teaching strategies designed to sensitize non-specialist students to the importance of the referent, to help them acquire the minimal background they will need to deal with texts in a given field and to enable them to reduce technical texts to their underlying referents.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".