In this article, we assess the availability of reproduction archives in political science. By “reproduction archive,” we mean the data and code supporting quantitative research articles that allows others to reproduce the computations described in the published article. We collect a random sample of quantitative research articles published in political science from 1995 to 2022. We find that—even in 2022—most quantitative research articles do not point to a reproduction archive. However, practices are improving. In 2014, when the DA-RT symposium was published in PS: Political Science and Politics, about 12% of quantitative research articles point to the data and code. Eight years later, in 2022, that has increased to 31%. This underscores a massive shift in norms, requirements, and infrastructure. Still, only a minority of articles share the supporting data and code. In 2014, Lupia and Alter wrote, “Today, information on the data production and analytic decisions that underlie many published works in political science is unavailable.” They could write the same today; much work remains to be done.

Many important questions in political science require the use of human-coded data or information that has been systematically ordered and quantified by a human being from qualitative sources. This article discusses challenges and recent innovations in collecting and documenting human-coded data. We review five datasets produced within the past 10 years and also reflect on our experiences in collecting a quarterly dataset that tracked state responses to the COVID-19 pandemic. We argue that scholars can deliberately produce and publish theoretically grounded human-coded data in an accessible format that promotes transparency, traceability, and readability. We highlight several ways that scholars are already doing this, including narratives, source lists, and coding justifications that enhance the quality of their human-coded datasets. We also discuss common issues during coding and how technological innovation through interactive web-based platforms can improve the documentation of coding decisions.

The intersection of conflict research and research ethics is already a complex and fraught one, particularly in exchanges between researchers from the Global North and researched communities from the Global South. There are many examples (and years) of exploitation, fraud, and violence in these exchanges, and more recent scholarship on fieldwork ethics has established new norms of reciprocal exchange rather than exploitation in these relationships. However, the outbreak of the COVID-19 pandemic, asymmetric access to health care, and global vaccine inequality has added yet another layer of complexity here. How do we continue move forward with the push to make our research exchanges ethical while dealing with the additional complexity of the pandemic? In this article, I reflect on the ethics of these exchanges and concerns around security for interlocutors in replacing in person fieldwork with virtual fieldwork, drawing from examples of my own doctoral dissertation research in the Pacific region of Colombia, which was interrupted by the outbreak of COVID-19. In this article, I describe how a turn to the digital archives helped mitigate additional ethical and security concerns that arose as a result of the pandemic.

This article investigates the impact of ChatGPT on student writing in the context of Model United Nations position papers, which require extensive research and concise articulation of policies on international issues. By comparing student-written papers and ChatGPT-generated papers, we find that whereas AI-generated papers received higher evaluations overall, award-winning student papers outperformed AI-generated papers. Furthermore, the quality of AI-generated writing is shown to depend on human input, emphasizing the importance of a human-centered approach to AI in education.