Yunfeng Gao

Yang Ding, Ning Zhang, Helen Bao et al.

While female representation in social sciences is increasing, systemic gender disparities may persist in research funding and academic performance. Some argue that female scholars now receive equal opportunities, yet evidence suggests that gender imbalances remain, particularly in specific research areas. This study examines 12,945 National Science Foundation (NSF)-funded principal investigators in social sciences from 2000 to 2019 to assess gender disparities in grant allocation, research topics, and post-award academic performance. Findings reveal a dual imbalance. First, despite similar overall funding success rates, female scholars remain underrepresented in high-impact and traditionally male-dominated research topics. Males dominate most funded topics, especially STEM-related ones, while female-led topics align with traditional gender stereotypes. Second, post-award performance patterns suggest that females outperform males in male-dominated fields, whereas males excel in female-dominated ones, undermining any presumed advantage of female scholars in their own research areas. These disparities contribute to the risk of both genders prematurely exiting the science pipeline. Furthermore, early-career experiences shape these outcomes asymmetrically: postdoctoral experience benefits both genders in female-dominated fields, with stronger effects for males, but disadvantages females in male-dominated fields by reducing their output and citation impact. Longer postdoctoral tenure enhances male researchers' citation impact across all fields but has mixed effects for females depending on field gender composition. These findings underscore the need for policies that address not just overall funding equality, but also gendered disparities across research topics and career trajectories.

Yunfeng Gao, Yuxuan Xiao, Jiaming Zhang et al.

Education research occupies a distinctive position in public science because it is expected to advance scholarly knowledge while also informing learning, teaching, participation, and workforce development. This study examines how the intellectual characteristics of NSF-funded education proposals are associated with the subsequent academic performance of funded scholars. Linking 8,715 NSF education awards from 1990 to 2020 with 84,519 publications by principal investigators, the analysis focuses on four major NSF education divisions that collectively span undergraduate and graduate levels, formal and informal learning environments, and inclusive educational initiatives. Proposal novelty is measured as semantic distance from prior funded projects within the same division, topical diversity as breadth across latent research themes, and intellectual orientation as theoretical, practical, or balanced. The results show that NSF education funding is consistently associated with higher publication output across divisions. However, this increase is not accompanied by stronger citation performance or higher journal-level visibility; citation and CiteScore estimates are often negative, particularly in later decades. Proposal novelty shows limited and uneven associations with post-award outcomes, whereas topical diversity is more clearly related to publication growth in some divisions but weaker citation-based performance in others. Balanced proposals that integrate theoretical and practical aims display the most favourable overall profile, combining positive publication associations with fewer negative citation-based patterns. These findings highlight the importance of evaluating education research funding through multiple academic outcomes and division-specific research contexts.

Jiaming Zhang, Yang Ding, Yunfeng Gao

In this study, we delve into the Structured State Space Model (S4), Change Point Detection methodologies, and the Switching Non-linear Dynamics System (SNLDS). Our central proposition is an enhanced inference technique and long-range dependency method for SNLDS. The cornerstone of our approach is the fusion of S4 and SNLDS, leveraging the strengths of both models to effectively address the intricacies of long-range dependencies in switching time series. Through rigorous testing, we demonstrate that our proposed methodology adeptly segments and reproduces long-range dependencies in both the 1-D Lorenz dataset and the 2-D bouncing ball dataset. Notably, our integrated approach outperforms the standalone SNLDS in these tasks.