STSS Coursework

Canonical theories of the structure of science construe scientific progress as a process of democratic debate among scientists.  However, these theories adopt either an overly idealistic or overly agnostic view about the role institutions serve in facilitating scientific debate and practices more generally.  This course will focus on how economic, political, social, and disciplinary forces structure the content and practices of science – including institutional racism as well as patterns of epistemic ignorance, injustice, and oppression.

As an emerging field of inquiry, the rhetoric of health and medicine approaches the study of health communication through the lens of critical theory and with the tools of rhetorical criticism. We will survey this emergent scholarship and discuss how health intersects with power, identity, policy, and activism. In doing so, this course will equip you with an awareness of what makes a rhetorical perspective distinct from and complementary to other approaches to studying health.

Provides a theoretical foundation for study in the area of communication technology and society by examining different contemporary theories of the social, political, and cultural implications of technological change. Takes a broad view of theories of communication innovations, tools, and technologies – including historical, critical, and comparative approaches.

Machine Learning (ML) and Artificial Intelligence (AI) mediated science is everywhere.  Scientists use these technologies to: synthesize ideas in the exponentially growing scientific literature; generate new hypotheses, data, analyses, code, figures, and narrative content; and, evaluate the credibility of a study’s claims by predicting it’s replicability and/or providing synthetic “peer review” scores and narrative content.  The starting point for this seminar will be identifying how these technologies have fundamentally ruptured science’s traditional structures for ensuring trustworthy scientific communication and evaluation.  We will then engage in a multi-week critique of ML’s ability to help scientists achieve one of their most revered epistemic and pragmatic goals: being able to identify whether some scientific claim about our physical and/or social world is “true.”