Knowledge Mapping of News Translation Studies: A Bibliometric Analysis
Bibliographic record
Abstract
To gain an in-depth understanding of news translation studies and provide insights for future research directions, this study conducts a bibliometric analysis using VOSviewer and CiteSpace. Based on 255 journal articles from the Web of Science database, covering all records up to December 31, 2023, influential authors, countries, and journals are identified. CiteSpace is used for evolutionary analysis to track the developmental trajectories of keywords, while VOSviewer performs co-occurrence analysis to reveal associations among keywords and research topic concentrations. Additionally, cited references, authors, and journals are explored to deepen the understanding of the theoretical foundations of news translation studies. The results indicate significant growth in news translation research over the past two decades, particularly after 2010, with Robert A. Valdeón emerging as a highly influential scholar. An enhanced interdisciplinary trend is revealed, and the proportion of core authors is lower than Price’s expected 50%, indicating the field is still in its early developmental stage. Furthermore, the application of artificial intelligence and machine translation in current literature remains sparse. The combined results from VOSviewer and CiteSpace enhance comprehension of news translation studies and pave the way for future advancements in both theoretical and practical realms.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.018 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.190 | 0.219 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".