Bibliographic record
Abstract
As corpus-based translation studies continues to expand, researchers have employed data analytic techniques from neighbouring disciplines, such as corpus linguistics, to explore a wider variety of research questions. The field has evolved from early frequency-based approaches to corpus-based translation studies to now include more advanced statistical analyses to understand the complex web of variables encapsulated by the translation process. Big data analytic techniques that originated in data analytics and related quantitative fields could be usefully applied to research questions in translation and interpreting studies. To assess their applicability, this article first outlines what distinguishes big data from general corpora in translation and interpreting studies, identifying how data volume, variety, and velocity are applicable properties to be considered in corpus-based translation and interpreting studies research. Then, the article presents three types of big data analysis techniques, namely crosslingual and multilingual data analysis, sentiment analysis, and visual analysis. These analyses are presented in conjunction with potential research areas that would benefit from these complementary analytical approaches. The article concludes with a discussion of the implications of big data analytics in corpus translation studies, while charting the trajectory of a more quantitative, corpus-based approach to translation studies.
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 | 0.120 | 0.281 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.022 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.022 | 0.045 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".