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Significance The number of manually replicated studies falls well below the abundance of important studies that the scientific community would like to see replicated. We created a text-based machine learning… Click to show full abstract
Significance The number of manually replicated studies falls well below the abundance of important studies that the scientific community would like to see replicated. We created a text-based machine learning model to estimate the replication likelihood for more than 14,000 published articles in six subfields of Psychology since 2000. Additionally, we investigated how replicability varies with respect to different research methods, authors 'productivity, citation impact, and institutional prestige, and a paper’s citation growth and social media coverage. Our findings help establish large-scale empirical patterns on which to prioritize manual replications and advance replication research.
Journal Title: Proceedings of the National Academy of Sciences of the United States of America
Year Published: 2023
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