Developing a Blueprint for a Technology-mediated Approach to Translation Studies
Bibliographic record
Abstract
As Austermühl (2001) put it over a decade ago, the use of information and communication technologies is afait accompliin the lives of today’s translators. Translation Studies (TS) have traditionally contemplated technologies only as supporting tools for translation practice, and translators’ tools have not enjoyed consideration as decisive actors in TS. Hence, their impact has been somehow underrepresented in the discipline. In the light of well-established translation paradigms (linguistic, functional, cognitive, sociological), we analyze the role played by technology. Most TS approaches are artifactual, this meaning that a rather simplistic and outdated distinction is made between translator minds and the tools they use. This paper proposes an instrumental approach to technologies within TS. In this, cohesive and mutual merging between translators and their technologies, both field-specific and generic tools lead us towards a concept that goes beyond purely linguistic or anthropocentric translation notions. A trans-human translation theoretical modeling is proposed to revisit TS paradigms in the context of the Information Society era. The trans-human translation closes the loop initiated by the fragmentation and dehumanization of first translation technologies, and envisages a stimulating future for translators, where they will use technological and social extensions in a creative and critical way.
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 | 0.056 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.008 | 0.056 |
| Scholarly communication | 0.024 | 0.033 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 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".