Towards two decades of journalistic translation research (2000-2019): a corpus-based bibliometric study of the Translation Studies Bibliography
Bibliographic record
Abstract
This paper provides a bibliometric analysis of journalistic translation research (JTR) from the past two decades. It is based on a corpus of 396 entries on journalistic translation and interpreting from the Translation Studies Bibliography (TSB) between 2000 and 2019. This study first consists of a bibliometric analysis of these entries from the aspects of author/editor, year, language, type, journal and publisher. It then explores prominent research topics and areas of focus based both on keywords provided by annotated entries in the TSB and on keywords and high-frequency nouns extracted from the abstracts as a result of corpus analysis. The study elaborates on various methodological approaches to studying journalistic translation according to the keywords provided in the entries of the TSB. The study found that: (1) most of the JTR was published by prestigious journals and publishers in English around 2010; (2) multimedia news about socio-cultural issues, published on various platforms, attracted the most attention from scholars; and (3) socio-cultural approaches have been the most prominent type of approach to journalistic translation over the past two decades. JTR research trends are also predicated according to the current development of the field.
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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 | 0.019 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.079 | 0.125 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".