Scientific papers are no longer read primarily as linear narratives by attentive human readers. They are increasingly filtered, summarized, compared, and evaluated by artificial intelligences that ingest papers as whole objects rather than as sequences of sentences. This shift has already occurred in practice, yet scientific writing largely continues to assume a human-centered reading model shaped by rhetorical motivation, implicit definitions, and shared disciplinary context. The result is a growing structural mismatch between how papers are written and how they are actually processed. This paper argues that scientific writing must adapt by treating papers as structured scientific objects rather than primarily as persuasive narratives. It introduces the concept of Structured Scientific Objects (JSCI, which is pronounced Jay-Sy) as a framework for making definitions, scope, assumptions, validation criteria, and failure modes explicit and machine-legible, while preserving explanatory prose for human readers. The paper emphasizes that this shift does not replace human judgment, creativity, or theory-building, but reorders priorities so that structure precedes persuasion. A central claim is that theories should be evaluated only against empirical measurements, not against other theories, particularly in an AI-mediated environment where theory-centric comparisons introduce bias and distort visibility. To support gradual adoption, the paper proposes a graded JSCI compliance model that allows papers to move incrementally toward greater structural completeness rather than enforcing a binary standard. Written deliberately in a traditional rhetorical style for a human audience, this paper functions as a transitional document. It situates JSCI within the historical trajectory of increasing scientific rigor and argues that treating papers as structured objects is not a radical departure, but a practical and inevitable response to the changing conditions under which scientific knowledge is read, evaluated, and extended.
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Stephen Euin Cobb
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Analyzing shared references across papers
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Stephen Euin Cobb (Fri,) studied this question.
www.synapsesocial.com/papers/698828d90fc35cd7a8848aba — DOI: https://doi.org/10.5281/zenodo.18506009