Bibliographic record
Abstract
The article sums up the principle trajectories of research in translation studies that are likely to be productive in the coming decades. I focus on six broad areas. The first encompasses attempts to define translation: this includes research as diverse as examinations of particular linguistic facets of translation, corpus studies of translation, descriptive historical studies, and analysis of think-aloud protocols. The second area of research pertains to the internationalization of translation, which challenges basic Western assumptions about the nature of translation and generates new case studies that shake the foundations of translation theory and practice as they are known at present. Changes in translation theory and practice associated with emerging technologies and globalization constitute the third research area to be discussed. The fourth strand is the application to translation of various interpretive perspectives based on frames from other disciplines. The last two branches of research have to do with the relationship of translation studies to cognitive science and neurophysiology. The article closes with some general observations about the implications for translation research as a whole and the structure of translation studies entailed by the six areas discussed.
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 | 0.065 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.022 | 0.030 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".