Contemporary scientific communication is increasingly confronted with a paradoxical situation: systems originally introduced to support academic integrity, including AI text detectors, plagiarism screening mechanisms, and automated stylistic assessment tools, are progressively functioning as instruments of probabilistic exclusion rather than reliable verification. Widely used AI detection systems such as GPTZero and Turnitin demonstrate persistent false positive behaviour when analysing highly formal academic prose, institutional reports, and manuscripts written by non-native English speakers. This paper argues that the current generation of algorithmic filtering systems, many of which remain conceptually dependent on statistical paradigms developed during the early 2000s, cannot be regarded as a sufficiently reliable mechanism for scientific validation. In response, an alternative framework is proposed: a transition from stylistic suspicion-based filtering towards factological verification grounded in methodological transparency, reproducibility, reference integrity, and expert evaluation. These principles form part of the conceptual infrastructure of Chronicle OS.
Oleksandr Purpurov (Mon,) studied this question.