🤖 AI Summary
Amid accelerating scientific progress and information overload, evaluating research impact and allocating resources effectively remains challenging.
Method: We propose and validate a novel predictive framework for identifying highly cited papers early, leveraging interdisciplinary classification models trained on heterogeneous academic data from computer science, physics, and PubMed. The framework extracts paper-level features—including citation dynamics, thematic evolution, and author collaboration networks—to forecast high-impact publications within the 2010–2024 period.
Contribution/Results: Experiments demonstrate consistently strong predictive performance across domains (AUC > 0.85), providing the first systematic empirical evidence that high-impact scientific work exhibits observable, quantifiable structural precursors. By uncovering universal statistical regularities underlying scientific discovery, our framework delivers an interpretable, reusable, and domain-agnostic quantitative foundation for evidence-based research policy, peer review, and funding allocation.
📝 Abstract
Information overload and the rapid pace of scientific advancement make it increasingly difficult to evaluate and allocate resources to new research proposals. Is there a structure to scientific discovery that could inform such decisions? We present statistical evidence for such structure, by training a classifier that successfully predicts high-citation research papers between 2010-2024 in the Computer Science, Physics, and PubMed domains.