Pengfei Yin

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.

Hengzhu Tang, Yanan Cao, Zhenyu Zhang et al.

Document-level RE requires reading, inferring and aggregating over multiple sentences. From our point of view, it is necessary for document-level RE to take advantage of multi-granularity inference information: entity level, sentence level and document level. Thus, how to obtain and aggregate the inference information with different granularity is challenging for document-level RE, which has not been considered by previous work. In this paper, we propose a Hierarchical Inference Network (HIN) to make full use of the abundant information from entity level, sentence level and document level. Translation constraint and bilinear transformation are applied to target entity pair in multiple subspaces to get entity-level inference information. Next, we model the inference between entity-level information and sentence representation to achieve sentence-level inference information. Finally, a hierarchical aggregation approach is adopted to obtain the document-level inference information. In this way, our model can effectively aggregate inference information from these three different granularities. Experimental results show that our method achieves state-of-the-art performance on the large-scale DocRED dataset. We also demonstrate that using BERT representations can further substantially boost the performance.