Restructuring scientific papers for human and AI readers

Scientific communication faces a dual crisis: exponential publication growth overwhelms human readers, and fragmented research practices block automated synthesis. AI-assisted writing exacerbates the volume problem, producing papers faster than they can be read. Behavioral and social sciences in particular suffer from incomparable stimulus databases, jingle–jangle measurement fallacies, and demographic blindness that conceals effect heterogeneity. Current AI tools aid comprehension and summarization yet cannot aggregate findings from incommensurable studies and risk amplifying biases when trained on unstructured, unverified text. We propose restructuring scientific papers for dual audiences: front-loaded narratives for time-pressed human readers, paired with machine-readable appendices containing executable code, standardized metadata, and ontologically mapped constructs. This design turns papers into queryable research environments where readers can interrogate data and rerun analyses, and where structured appendices enable automated verification of statistical methods and AI-assisted peer review grounded in executable rather than narrative claims. Such papers become nodes in continuously updated evidence networks: each publication automatically contributes effect sizes to real-time meta-analyses, with corrections and retractions propagating through dependent analyses. Widespread adoption will require institutional recognition of structured documentation as essential scholarly output and computational infrastructure that serves both human comprehension and machine analysis.