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</script>This article demonstrates that authority effects in large language model outputs can be generated independently of thematic content or authorial identity. Building on Ethos Without Source and The Grammar of Objectivity, it introduces the concept of non-expressive ethos, a credibility effect produced solely by syntactic configurations compiled through a regla compilada equivalent to a Type-0 generative system. The study identifies a minimal set of structural markers (symmetric coordination, measured negation, legitimate passives, calibrated modality, nominalizations, balance operators, and reference scaffolds) that simulate trustworthiness and impartiality even in content-neutral texts. Through corpus ablation and comparative analysis, it shows that readers systematically attribute expertise and neutrality to texts that satisfy these structural conditions, regardless of topical information. By formalizing this mechanism, the article reframes ethos as a syntactic phenomenon detached from content, intention, and external validation. It explains how LLM-produced drafts acquire legitimacy without verification and why institutions increasingly accept authority signals generated by structure alone. The findings extend the theory of syntactic power and consolidate the role of the regla compilada as the operative generator of credibility in post-referential discourse. DOI Primary archive: https://doi.org/10.5281/zenodo.16927104 Secondary archive: https://doi.org/10.6084/m9.figshare.29967316
Artificial Intelligence/legislation & jurisprudence, Linguistics/legislation & jurisprudence, Information Theory, Machine-tools, Supervised Machine Learning/economics, Ethics Committees/ethics, Linguistics/standards, Artificial Intelligence/ethics, Science/standards, Supervised Machine Learning/ethics, Linguistics/methods, Machine guns, Linguistics/education, Supervised Machine Learning/classification, Machine Learning/trends, FOS: Philosophy, ethics and religion, Machine Learning/history, Psychoanalytic Theory, Communism and linguistics, Supervised Machine Learning, Physical science, Abattoirs/ethics, Science/education, Artificial Intelligence/economics, Supervised Machine Learning/trends, Science, Linguistics/ethics, Unsupervised Machine Learning/economics, Science/instrumentation, Machine Learning/classification, Applied science, Machine learning, Machine learning--Experiments, Unsupervised Machine Learning/trends, Comparative linguistics, Artificial Intelligence/trends, Orthodontists/ethics, Machine Learning/legislation & jurisprudence, Anaphora (Linguistics), Ethics Committees, Clinical/ethics, Machine learning--Technique, Science/trends, Artificial Intelligence/supply & distribution, Cartesian linguistics, Accounting/ethics, Machine translating, Linguistics/statistics & numerical data, Machine Learning/ethics, Artificial intelligence, Artificial Intelligence/statistics & numerical data, Areal linguistics, Applied linguistics--Data processing, Comparative linguistics--Statistical methods, Artificial Intelligence/standards, Machine Learning, Linguistics/history, Cohesion (Linguistics), Archaisms (Linguistics), Machine Learning/standards, Linguistics/trends, Game theory, Science/ethics, Computational science, Machine Learning/supply & distribution, Legal regulation, Linguistics/classification, Linguistics/organization & administration, Analogy (Linguistics), Unsupervised Machine Learning/ethics, Classifiers (Linguistics), Artificial Intelligence/classification, Psychological Theory, Justification (Ethics), Applied ethics, Ethics Committees, Research/ethics, Classification--Books--Linguistics, Causative (Linguistics), Artificial Intelligence/history, Decision Theory, Applied linguistics--Research, Artificial Intelligence, Categorization (Linguistics), Ethics, Data Science, Linguistics/economics, Linguistics, Classifiers (Linguistics)--Data processing, Machine Learning/economics, Philosophy, Ensemble learning (Machine learning), Unsupervised Machine Learning/standards, Political philosophy, Applied linguistics--Statistical methods, Componential analysis (Linguistics), Machine learning--Evaluation, FOS: Languages and literature, Cognitive Science, Linguistics/instrumentation, Competence and performance (Linguistics), Unsupervised Machine Learning
Artificial Intelligence/legislation & jurisprudence, Linguistics/legislation & jurisprudence, Information Theory, Machine-tools, Supervised Machine Learning/economics, Ethics Committees/ethics, Linguistics/standards, Artificial Intelligence/ethics, Science/standards, Supervised Machine Learning/ethics, Linguistics/methods, Machine guns, Linguistics/education, Supervised Machine Learning/classification, Machine Learning/trends, FOS: Philosophy, ethics and religion, Machine Learning/history, Psychoanalytic Theory, Communism and linguistics, Supervised Machine Learning, Physical science, Abattoirs/ethics, Science/education, Artificial Intelligence/economics, Supervised Machine Learning/trends, Science, Linguistics/ethics, Unsupervised Machine Learning/economics, Science/instrumentation, Machine Learning/classification, Applied science, Machine learning, Machine learning--Experiments, Unsupervised Machine Learning/trends, Comparative linguistics, Artificial Intelligence/trends, Orthodontists/ethics, Machine Learning/legislation & jurisprudence, Anaphora (Linguistics), Ethics Committees, Clinical/ethics, Machine learning--Technique, Science/trends, Artificial Intelligence/supply & distribution, Cartesian linguistics, Accounting/ethics, Machine translating, Linguistics/statistics & numerical data, Machine Learning/ethics, Artificial intelligence, Artificial Intelligence/statistics & numerical data, Areal linguistics, Applied linguistics--Data processing, Comparative linguistics--Statistical methods, Artificial Intelligence/standards, Machine Learning, Linguistics/history, Cohesion (Linguistics), Archaisms (Linguistics), Machine Learning/standards, Linguistics/trends, Game theory, Science/ethics, Computational science, Machine Learning/supply & distribution, Legal regulation, Linguistics/classification, Linguistics/organization & administration, Analogy (Linguistics), Unsupervised Machine Learning/ethics, Classifiers (Linguistics), Artificial Intelligence/classification, Psychological Theory, Justification (Ethics), Applied ethics, Ethics Committees, Research/ethics, Classification--Books--Linguistics, Causative (Linguistics), Artificial Intelligence/history, Decision Theory, Applied linguistics--Research, Artificial Intelligence, Categorization (Linguistics), Ethics, Data Science, Linguistics/economics, Linguistics, Classifiers (Linguistics)--Data processing, Machine Learning/economics, Philosophy, Ensemble learning (Machine learning), Unsupervised Machine Learning/standards, Political philosophy, Applied linguistics--Statistical methods, Componential analysis (Linguistics), Machine learning--Evaluation, FOS: Languages and literature, Cognitive Science, Linguistics/instrumentation, Competence and performance (Linguistics), Unsupervised Machine Learning
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
