Data and code for Large Language Models in Translation Studies: A Bibliometric and Systematic Review of Cognitive and Professional Reorganization
Year: 2026
Type: dataset
Abstract: This dataset and code archive supports the study “Large Language Models in Translation Studies: A Bibliometric and Systematic Review of Cognitive and Professional Reorganization.” It contains openly redistributable derived data, documentation, figures, and analysis code for a combined bibliometric, topic-model, thematic, and systematic review of large language models in translation studies during 2016–2026. The bibliometric corpus comprises 1,607 unique records retrieved from Web of Science and Scopus, and the nested systematic-review corpus comprises 92 English-language full-text reports containing primary human psychological or behavioral evidence. The synthesis examines translator cognition, trust and reliance, agency, professional identity, attitudes, affect, learning, and authorship.The archive includes aggregate production, source, country, keyword, and network tables; topic-model selection outputs; the included-study inventory and bibliography; construct and theme matrices; coder-agreement and appraisal outputs; full-text exclusion reasons; an evidence matrix and claim–evidence calibration table; exact search and review documentation; six publication-quality figures; R and Python scripts; a data dictionary; and file-level checksums. The analytical dataset was frozen as version 0.7.0, while this deposit and its documentation are version 0.9.0.Original Web of Science and Scopus exports, licensed record-level metadata, complete publisher PDFs, and internal coder workbooks are excluded because of database-licensing, copyright, and research-governance constraints. Researchers with lawful database access can reconstruct the record corpus using the deposited search strings, dates, deduplication information, methods, and code. The archive contains no participant-level personal data.
Author: Wang, Xiongfei
Institution: Guangdong University of Foreign Studies
Language: English
Subfield: Language and Linguistics
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: green
Award: G4865243338
OpenAlex ID: W7212423364
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