421 papers
This study employs unsupervised machine learning on population pyramids from 200 countries to empirically identify distinct global demographic configurations and national trajectories, revealing that while age structures and historical pathways are related, they are not interchangeable and offer a complementary framework to traditional demographic transition theory.
This study employs a reproducible Monte Carlo simulation of 700 synthetic universities to demonstrate that institutional rankings vary significantly across OpenAlex, Scopus, and Web of Science due to database-specific mechanisms like coverage and citation scaling, revealing that high system-level concordance masks consequential institution-level rank instability and regional biases.
This computational text analysis of research-project documents from major funding agencies reveals that the rapid inflation of vague "umbrella words" like "diversity" and "sustainable" is driven by funder mandates and political shifts rather than scientific necessity, leading to reduced lexical specificity and a semantic drift from concrete objects to abstract goals.
This paper introduces a pairwise, nonparametric permutation testing framework that distinguishes competitor-specific judge bias from benign style differences within the ISU Judging System, successfully identifying significant and outcome-determinative bias in 2026 Olympic Ice Dance and other recent competitions.
This empirical study demonstrates that a dual-criterion variable inclusion strategy, which combines baseline-imbalance and univariate-outcome testing, effectively mitigates confounder omission bias in small-sample trauma cohorts, leading to more accurate risk-factor identification and robust prediction models compared to traditional single-threshold screening.
This review of 40 health studies in Somalia reveals that while research activity is growing, the field is characterized by a predominance of cross-sectional designs and descriptive statistics, with significant gaps in rigorous sampling, advanced analytical techniques, and transparent reporting practices that ultimately limit the credibility and utility of the evidence.
This paper establishes improved non-asymptotic bounds for saddlepoint approximations of high-dimensional random vector sums, demonstrating a error rate that surpasses previous results and enabling refined local central limit theorems and corrected Gaussian approximations under verifiable conditions for diverse distributions.
This paper employs a normative optimal-control model to demonstrate that the widely recommended 98% vocabulary coverage target is optimal in only a small fraction of parameter scenarios, with the true optimal allocation driven primarily by the free rehearsal provided by reading, vocabulary retention rates, and the study horizon rather than grammar-specific parameters.
This review synthesizes recent advancements in integrating artificial intelligence techniques with spline smoothing to address challenges in knot selection and scalability, offering a comprehensive taxonomy of hybrid methods, theoretical insights, and practical applications across biomedical and financial domains.
This study demonstrates that while matching sample sizes can improve the correct ordering of mutual information estimates in specific scenarios, it often fails to reduce overall comparison bias and may even impair accuracy, highlighting the significant risks of finite-sample bias when comparing mutual information across unequal sample sizes.