427 papers
This scoping review and case study of Syria reveal that conventional statistical methods systematically render people with disabilities invisible in humanitarian contexts by failing to capture complex, intersectional realities, thereby necessitating a paradigm shift toward more disaggregated and flexible data collection to ensure inclusive aid delivery.
This study demonstrates that machine-learning models trained on post-wildfire damage data, while accurate within specific events, fail to generalize across different fires because the protective or detrimental effects of home-hardening features (such as single-pane windows) are not stable and can even reverse depending on the fire context, thereby cautioning against using pooled observational data to derive universal hardening policies.
This Monte Carlo study demonstrates that in writing research pretest–posttest designs, varying the number of tasks and score reliability produces systematic trade-offs in performance and cost rather than a uniform ranking, thereby advocating for multi-criteria evaluation and mechanism-focused validation over reliance on single metrics.
This paper introduces a novel heteroscedastic regression framework based on the exponentiated generalized normal distribution with two shape parameters, validating its robustness through Monte Carlo simulations and demonstrating its practical application in modeling persimmon fruit coloration.
This study employs spatial econometric and Bayesian hierarchical models to analyze homicides in Brazil's Metropolitan Region of Campinas (2017–2022), revealing that socioeconomic factors like income and dependency ratios, alongside strong spatial clustering, significantly influence violence patterns and necessitate territory-sensitive public security policies.
This paper proposes a causal inference framework that decomposes randomized trial outcomes into biological effects () and preference-mediated choice effects (), arguing that formally identifying both components resolves ambiguity caused by nonadherence and crossover while providing a more complete understanding of treatment efficacy in preference-sensitive care.
This paper introduces a large-scale probabilistic framework that integrates citation structures and semantic trajectories to identify five distinct latent regimes of scientific evolution, demonstrating that semantic drift is the strongest predictor of regime transitions and offering a more nuanced understanding of scientific dynamics beyond traditional citation impact.
This paper revisits the validity of bibliometric indicators under falling production costs by demonstrating that while relative indicators remain robust under uniform cost changes, heterogeneous access requires regime-conditioned reporting using characteristic estimators to avoid misclassification when the underlying distribution shape shifts.
This paper argues that the widely used log(x + 1) transformation introduces substantial bias and misleading inferences, particularly for exponentially distributed data, and recommends abandoning it in favor of alternative numerical methods like Generalized Linear Models (GLM).
This paper proposes and demonstrates a dataset-structured framework for synthesizing machine-learning prediction model evidence that redefines evidence units from publications to validation exercises, thereby correcting misrepresentations of independence and performance that arise from conventional pooling methods.