The study asks whether Claude 3 Opus, Gemini 1.5 Flash, and Mistral AI Large preserve LIWC patterns from human scientific abstracts when rewriting them, and whether differences between exclusively male- and female-authored texts reappear or grow. It starts from 3,390 randomly selected CORE abstracts: 418 are classified as female-only, 946 as male-only, and 2,026 as mixed-gender through `gender-extractor`, which infers gender from names. Each model receives the same zero-shot rewrite prompt with default parameters. LIWC-22 extracts 35 lexical, cognitive, affective, social, cultural, and motive indicators. For each indicator, Pearson correlation compares the original and rewritten values over 3,390 pairs; the gender analysis excludes the mixed majority and compares 946 against 418 through independent t-tests, separately for originals and each model. Published diagonal correlations are positive and range roughly from 0.22 to 0.92: word count correlates 0.35 for Claude, 0.80 for Gemini, and 0.86 for Mistral; discrepancy is 0.22 for all three, while anxiety, ethnicity, and technology often exceed 0.85. The paper reports p<0.05 for every non-NaN correlation and concludes that models preserve human textual traits. This only establishes covariation of LIWC counts in rewrites of the same content; it does not measure psychological personality, semantic equivalence, or lack of distortion. In the gender table, originals show, among others, t=-5.86 for word count, 3.93 for Tone, -6.74 for insight, -2.27 for cause, 3.82 for positive emotion, 5.70 for politeness, and 2.61 for risk. LLM outputs show new or clearer significant differences for achievement, conflict, and curiosity, while causation is no longer significant. Those shifts are descriptively interesting, but the study never directly tests amplification: it does not compare LLM-minus-original change between groups or estimate an interaction. The most serious error is interpreting t statistics as ratios: t=-5.86 becomes '5.86 times more words,' t=-6.74 becomes 'seven times more insight words,' and t=5.70 becomes 'five times more polite.' A t statistic is not a ratio of means; without means, dispersions, and effect sizes, no 'times more' statement follows. The analysis also lacks multiplicity correction, exact p values, intervals, and released data. The prompt contains neither gender nor author names: the model sees only the abstract, so an inherited difference may reflect preservation of topic or style rather than a model-introduced stereotype. The published version acknowledges class imbalance, disciplinary confounding, and absence of longitudinal analysis. Without the CORE subset, generations, code, exact model versions, and LIWC outputs, the work cannot be recomputed. The defensible contribution is an exploratory paired-LIWC comparison scheme; the evidence does not quantify bias magnitudes or establish that LLMs cause or amplify a gender gap.
Research question
To what extent do three LLMs preserve the LIWC profile of scientific abstracts when rewriting them, and do they reproduce, reduce, or amplify the differences observed between texts classified as exclusively male or female authorship?