Mining Scholarly Impact: A Career-Adaptive Higher-Order Aggregation Framework for Multi-Domain Researcher Classification

Assessing scholarly contribution across disciplines is challenging because bibliometric indicators capture different aspects of research activity and citation practices vary by field. This study evaluates a weighted cubic-mean composite (Hr) and a career-adaptive extension (DHr) across civil engineering, computer science, mathematics, and neuroscience. The analysis considers 4479 researchers and uses recognition by professional societies as an observable benchmark, while recognizing that awards are imperfect proxies for research quality. reduce selection optimism, we performed nested cross-validation with researcher-level grouping, selecting candidate indicators and metric pairs within inner folds and evaluating the fixed DHr reference formulation on held-out outer folds. On out-of-fold predictions, Hr AUCs were 0.556, 0.692, 0.768, and 0.713, while DHr AUCs were 0.559, 0.690, 0.767, and 0.712 across the four domains, respectively. Paired bootstrap confidence intervals for the Hr–DHr AUC differences included zero in every domain; therefore, these results do not establish that career-adaptive weighting improves discrimination over static aggregation. screening and metric-pair selection were conducted separately in each discipline, these AUCs reflect within-domain validation across four datasets and do not demonstrate transfer to an unseen discipline. The findings support further investigation of composite bibliometric indicators, but independent validation, transparent benchmarking, and fairness assessment are needed before consequential use.