Jie Hua

Likun Cao, Jie Hua, James Evans

This paper investigates mechanisms underlying scientific stratification in the transition from elite to mass science. Existing scholarship has examined stratification through the Matthew effect framework, but this approach is increasingly limited as mass, team-based research becomes dominant. While scientists now share institutions and lineages, substantial career outcome differences remain unexplained. We propose integrating demographic concepts into science studies. Drawing parallels between biological families and scholarly lineages as fundamental reproductive units, we adapt the birth order concept to examine how doctoral student sequence within a lineage shapes career trajectories. Using data on over one million U.S. doctoral graduates, we find that later students of the same advisor systematically underperform earlier ones across multiple achievement dimensions, both short and long term. Examining underlying mechanisms reveals that although advisors invest comparable resources in all students, later students receive less cognitive stimulation from mature scholars than peers and specialize in narrower niches under peer differentiation pressure. Both of these factors constrain intellectual development and subsequent success. By introducing a demographic framework, this paper offers new perspectives on scientific stratification and demonstrates how demographic concepts can fruitfully analyze broader social and epistemic systems.

Jie Hua, Chenguang Liu, Tomasz Kalbarczyk et al.

Advances in mobile computing capabilities and an increasing number of Internet of Things (IoT) devices have enriched the possibilities of the IoT but have also increased the cognitive load required of IoT users. Existing context-aware systems provide various levels of automation in the IoT. Many of these systems adaptively take decisions on how to provide services based on assumptions made a priori. The approaches are difficult to personalize to an individual's dynamic environment, and thus today's smart IoT spaces often demand complex and specialized interactions with the user in order to provide tailored services. We propose rIoT, a framework for seamless and personalized automation of human-device interaction in the IoT. rIoT leverages existing technologies to operate across heterogeneous devices and networks to provide a one-stop solution for device interaction in the IoT. We show how rIoT exploits similarities between contexts and employs a decision-tree like method to adaptively capture a user's preferences from a small number of interactions with the IoT space. We measure the performance of rIoT on two real-world data sets and a real mobile device in terms of accuracy, learning speed, and latency in comparison to two state-of-the-art machine learning algorithms.