What conditions enable novel intellectual contributions to diffuse and become integrated into later scientific work? Prior work tends to focus on whole cultural products, such as patents and articles, and emphasizes external social factors as important. This article focuses on concepts as reflections of ideas, and we identify the combined influence that social factors and internal intellectual structures have on ideational diffusion. To develop this perspective, we use computational techniques to identify nearly 60,000 new ideas introduced over two decades (1993 to 2016) in the Web of Science and follow their diffusion across 38 million later publications. We find new ideas diffuse more widely when they socially and intellectually resonate. New ideas become core concepts of science when they reach expansive networks of unrelated authors, achieve consistent intellectual usage, are associated with other prominent ideas, and fit with extant research traditions. These ecological conditions play an increasingly decisive role later in an idea’s career, after their relations with the environment are established. This work advances the systematic study of scientific ideas by moving beyond products to focus on the content of ideas themselves and applies a relational perspective that takes seriously the contingency of their success.

Stratification and rerandomization are two well-known methods used in randomized experiments for balancing the baseline covariates. Renowned scholars in experimental design have recommended combining these two methods; however, limited studies have addressed the statistical properties of this combination. This article proposes two rerandomization methods to be used in stratified randomized experiments, based on the overall and stratum-specific Mahalanobis distances. The first method is applicable for nearly arbitrary numbers of strata, strata sizes, and stratum-specific proportions of the treated units. The second method, which is generally more efficient than the first method, is suitable for situations in which the number of strata is fixed with their sizes tending to infinity. Under the randomization inference framework, we obtain the asymptotic distributions of estimators used in these methods and the formulas of variance reduction when compared to stratified randomization. Our analysis does not require any modeling assumption regarding the potential outcomes. Moreover, we provide asymptotically conservative variance estimators and confidence intervals for the average treatment effect. The advantages of the proposed methods are exhibited through an extensive simulation study and a real-data example.