Steven Zhou
Steven Zhou is an Assistant Professor of Psychological Science at Claremont McKenna College. His research and teaching focus on quantitative methods and data science applied to organizational phenomena such as leadership, personality, and career development. He uses methods ranging from traditional multivariate statistics to natural language processing (NLP) and AI, with an eye toward producing research with real-world impact for everyday leaders, managers, and employees. He earned his Ph.D. in Organizational Psychology from George Mason University with a certificate in Computational Social Sciences. He has previous industry work experience in HR, nonprofit management, and institutional data analytics.
Claremont McKenna College
LinkedIn: https://www.linkedin.com/in/szzhou4
Sessions
Predatory journals threaten research integrity by imitating the signals of legitimate publishing, yet little empirical work examines whether readers can reliably detect them or identify the cues driving credibility judgments. This ongoing experimental study investigates how individuals evaluate scientific research when journal-level signals vary in legitimacy, holding research content constant while manipulating journal labels. Participants assess perceived credibility, trust, and intended use of the research, alongside confidence in their evaluations and the heuristics when making judgments.
Across participants, journal labels shape credibility assessments, with respondents relying on surface-level cues such as journal reputation, citations, and author credentials. The study is being expanded to include members of the general public, higher education, and journalists, enabling comparison across audiences that play central roles in consuming, teaching, and communicating research. Together, this work aims to inform research-literacy interventions and improve how scientific credibility is evaluated in expanding predatory publishing.
In this Unconference session, we seek to discuss ways to facilitate more interdisciplinary research collaboration, which is largely promoted as beneficial but comes with limited structural support to do so. As a starting point for discussion, we will present a free app developed to foster research collaboration: ResearchRendezvous. Traditional methods of academic networking are ineffective and inefficient, with no streamlined methods of sharing budding research ideas and finding collaborators to help bring ideas to life. Our app allows users at conferences and beyond to quickly search through others’ research ideas, find ones they are interested in collaborating on, and open a chat to establish lines of communication – much like a dating app, but for research ideas! Participants will get the chance to pilot test the app during this session, discuss its use potential, give feedback on how to improve the app, and discuss additional ideas to facilitate interdisciplinary collaboration.