Restructuring scientific papers for human and AI readers

Scientific communication faces a dual crisis: exponential publication growth—now accelerated by AI-assisted writing—overwhelms human readers and reviewers, while fragmented research practices block automated synthesis. The behavioral and social sciences in particular suffer from incomparable stimulus databases, jingle–jangle measurement fallacies (same label, distinct constructs; different labels, same construct), and contextual blindness that conceals effect heterogeneity. Current AI tools can summarize papers but cannot synthesize findings across incommensurable studies; they also risk amplifying biases when trained on unstructured, unverified text. I propose restructuring scientific papers for dual audiences: front-loaded narratives for time-pressed human readers, paired with research-object packages containing executable code, semantic annotations, and tidy trial-level data. This design makes papers queryable research environments: readers can interrogate data and probe analytic choices in real time, while research-object packages enable automated verification and AI-assisted peer review grounded in executable evidence rather than narrative claims. Such papers become nodes in continuously updated evidence networks: each publication automatically contributes effect sizes to living, versioned 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.