Pragmatic Profiling of Textual Genres: Characterizing Subjectivity in Human and AI-generated Spanish Texts

Resumen

The use of Generative AI for text creation has become a standard practice; however, a significant stylistic and pragmatic gap remains between human authorship and synthetic Spanish discourse. This study employs a multilevel analysis, based on morphosyntactic metrics and subjectivity markers, to compare scientific Abstracts, News, and Reviews produced by humans and models such as GPT-5, Llama-3.3, and Mistral 3. Results reveal that these models exhibit a hyper-nominal and rigid style, prioritizing informational density over the discursive flexibility of human writing. At the pragmatic level, a homogenization of subjectivity is observed: models fail to replicate the polarized extremes of human style, such as the technical specificity of Abstracts or the personal participation found in Reviews. Finally, cross-domain classification experiments confirm that this reduction in genre-specific subjective markers diminishes the stability of synthetic genres, evidencing a loss of linguistic identity in AI-generated texts.