Bibliographic record
Abstract
The introduction of digital computers, information and communication technologies (ICTs), and the Internet/Web has broadened the scope of communication globally in ways unprecedented in human history. The “digital world” implies more than the technical and instrumental aspects and usage of technology; it equally involves our tangible human social engagement and interface with the tools and technologies themselves. The relevance of digital studies to translation studies, and vice versa, is substantial. Both fields intrinsically deal with language, information, and communication and are inextricably linked to technology. After a brief introduction, the article highlights first the essential informational and communicational foundation of technology development that intertwined with histories of translation technology. The convergence of these multiple histories has led to today’s 24/7 digital infrastructure. It then considers the social and cultural facets of the digital world, presenting research areas in digital studies that can be explored in relation to translation studies. While the existing analytical and critical approaches to researching translation can arguably be extended and transposed to include elements of the contemporary digital context, there are also compelling and legitimate reasons for contextualizing translation within the broader, global communication universe, positioning it wholly within the digital sphere.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.033 |
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".