No linguistic borders ahead? Looking beyond the knocked-down language barrier
Bibliographic record
Abstract
The article deals with the concept of borders and barriers in considering scenarios where the linguistic barrier is eventually lifted by technology one day. It begins with reflections on the biblical narrative of the Tower of Babel as an ancient representation of the concept of linguistic barriers between language communities. It gives numerous examples of the uptake of this narrative, from Translation Studies, to project calls and the marketing statements of machine translation technology. In the following section, examples of existing technology are presented, which could be considered as a first generation of automatic translation/interpretation systems. In the main section, several trends are predicted for both the translators’ profession and general economic/business/political/societal developments. The consequences are anticipated of a situation where ordinary cross-language communication will eventually have been almost fully taken over by automated systems. The article points to both the technology’s positive potential and, by showing the eventual risks involved, it equally rejects an attitude of the technology’s uncritical uptake. The article closes by pointing to the ethical dimension of machine translation systems linked with their types of uses and the choices reserved for their users.
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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.061 |
| Scholarly communication | 0.018 | 0.044 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".