Restructuring scientific papers for human and AI readers

Scientific communication faces a dual crisis: exponential publication growth overwhelms human readers while fragmented research practices prevent automated knowledge synthesis. This crisis is exacerbated by AI-assisted writing—producing papers faster than they can be consumed. Behavioral and social sciences, in particular, suffer from incomparable stimulus databases, jingle–jangle measurement fallacies, and demographic blindness that conceals effect heterogeneity. While AI tools support comprehension and summary assistance, they 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 transforms papers into queryable research environments where readers can directly interrogate data and rerun analyses. Structured appendices enable automated verification of statistical methods and AI-assisted peer review grounded in executable rather than narrative claims. These restructured papers become nodes in continuously updating evidence networks. Each publication automatically contributes effect sizes to real-time meta-analyses, with corrections and retractions propagating instantly through dependent analyses. Widespread adoption requires institutional recognition of structured documentation as essential scholarly output and computational infrastructure that serves both human comprehension and machine analysis.