When English Predicts Itself: A Corpus-Based Study of Lexical Homogenization in AI-Mediated Academic Discourse
DOI:
https://doi.org/10.63345/Keywords:
lexical homogenization, large language models, academic discourse, corpus linguistics, stylometry, predictabilityAbstract
The rapid adoption of large language models (LLMs) as writing aids in scholarly work has raised concerns that academic English is converging toward a narrower, more predictable register. This study examines lexical homogenization in academic discourse by analysing a stratified corpus of 48,000 English-language abstracts published between 2019 and 2024 across four disciplines. Using a validated lexicon of style-marker words previously associated with LLM-assisted text, alongside type-token ratio, self-similarity (self-BLEU), and language-model perplexity as complementary measures of predictability, the study finds a sharp, statistically significant shift in academic vocabulary beginning in 2023. Marker word density per 10,000 words rose from 3.6 in 2022 to 11.4 in 2024, while lexical diversity declined and cross-abstract textual similarity increased. Regression analysis shows that marker word density is a strong predictor of reduced perplexity under a pre-2022 reference language model (R² = 0.61), indicating that recent academic writing has become measurably more predictable, in effect allowing English to anticipate itself. Disciplinary comparison shows that technology-adjacent fields exhibit the largest homogenization effect. These findings support the hypothesis that AI-mediated writing assistance, while improving surface fluency, is narrowing the stylistic range of scholarly English.
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