Effect of Technological Developments on Ethical Position of Translator
Bibliographic record
Abstract
The objective of this essay is to examine and discuss the effect of recent technological developments on ethical position of translator. The associations between technology and the process of translation is a section of the greater discussion regarding the impacts that technology has on language. Presently, the career of interpreting and translating and its different branches, for example localization, are alleged to be under amplified threat from technological developments and practices such as crowdsourcing (Baker & Maier, 2011). The importance currently assigned to the effects of recent technology on the ethical position of the translator emanates from the fact that there are currently several emergent translation technologies, for example, collaborative translation management systems, translation memories and data-based machine translation, which transform the social links, professional views and thought patterns of the translator (Pym, 2001). In addition to examining the relationship between technology and translation, this essay will also assess the ethical questions posed by technology for translators. At the end of the essay are a conclusive summary of the entire discussion and an alphabetical list of the references cited herein.
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 | 0.043 | 0.229 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".