My research applies insights from organizational sociology to trace whether and how rebel movements transform into successful political parties in the aftermath of civil war. I use a multi-method approach—combining statistical analysis on a novel dataset with process tracing in three cases: El Salvador, Mozambique, and Sierra Leone. I show that, much like corporations, organizational diversity facilitates resilience, adaptation, and transformation in rebel groups.
My broader substantive interests lie at the intersection of international security, civil conflict, and democratization, which I study through an organizational lens.
I also work on developing both quantitative and qualitative research methods. I'm especially interested in process tracing, conceptualization, and developing principled inclusion criteria for large-N datasets.
Street art in Florence, Italy graciously reminding me what I should be doing instead of photographing street art in Florence, Italy.
PUBLICATIONS + WORKING PAPERS
Parkinson, Sarah E. and Sherry Zaks. 2018. "Militant and Rebel Organization(s)." Comparative Politics. 50(2): 271–290 [Download Paper]
Zaks, Sherry. 2017. "Relationships Among Rivals (RAR): A Framework for Analyzing Contending Hypotheses in Process Tracing." Political Analysis. 25(3): 344–362. [Download Paper]
Abstract: Methodologists and substantive scholars alike agree that one of process tracing’s foremost contributions to qualitative research is its capacity to adjudicate among competing explanations of a phenomenon. Existing approaches, however, only provide explicit guidance on dealing with mutually exclusive explanations, which are exceedingly rare in social science research. I develop a tripartite solution to this problem. The Relationships among Rivals (RAR) framework (1) introduces a typology of relationships between alternative hypotheses, (2) develops specific guidelines for identifying which relationship is present between two hypotheses, and (3) maps out the varied implications for evidence collection and inference. I then integrate the RAR framework into each of the main process-tracing approaches and demonstrate how it affects the inferential process. Finally, I illustrate the purchase of the RAR frame- work by reanalyzing a seminal example of process-tracing research: Schultz’s (2001) analysis of the Fashoda Crisis. I show that the same evidence can yield new and sometimes contradictory inferences once scholars approach compar- ative hypothesis testing with this more nuanced framework.
Abstract: An emerging trend in research on militant groups asks how structures, dynamics, and relationships within these organizations influence key wartime and postwar outcomes. While the analytical pivot toward organizations advances the field in essential ways, scholars still lack a unified conceptual approach to organization-centric analyses of militancy. This article distills four key dimensions for analysis from organizational sociology: roles, relations, behaviors, and goals. It then reviews four new works on militant organizations and outlines their place in this emergent research trajectory. These books, we argue, underscore how situating research at the organizational level sheds new light on political outcomes such as rebel resilience, social service provision, and deployment of violence. We then highlight two related and promising organizational research agendas for future studies.
"Do we know it when we see it? (Re-)Conceptualizing Rebel-to-Party Transformation"
(Invited to Revise and Resubmit at the Journal of Peace Research)
Abstract: In a recent article, I argued that the Bayesian process tracing literature exhibits a persistent disconnect between principle and practice. In their response, Bennett, Fairfield, and Charman raise important points and interesting questions about the method and its merits. This letter breaks from the ongoing point-by-point format of the debate by asking one question: In the most straightforward case, does the literature equip a reasonable scholar with the tools to conduct a rigorous analysis? I answer this question by walking through a qualitative Bayesian analysis of the simplest example: analyzing evidence of a murder. Along the way, I catalogue every question, complication, and pitfall I run into. Notwithstanding some important clarifications, I demonstrate that aspiring practitioners are still facing a method without guidelines or guardrails.
Zaks, Sherry. 2016. "The Logic of Process Tracing: Contributions, Pitfalls, and Future Directions" In Handbook of Research Methods and Applications in Political Science. Hans Kerman & Jaap J. Woldendorp, eds.
Abstract: The wealth of literature on process tracing over the last thirty years is both a blessing and a curse for the researcher who comes to the method anew. On the one hand, she finds a variety of well-explicated guidelines on how to conduct her research; on the other hand, she is faced with the burden of choosing which set of guidelines to follow. Ultimately, the major innovations in process-tracing are not always mutually compatible. The goal of this chapter is to synthesize the literature and provide scholars with a concrete evaluation of the progress, pitfalls, and remaining gaps in the method.
Zaks, Sherry. 2021. "Updating Bayesian(s): A Critical Evaluation of Bayesian Process Tracing."
Political Analysis. 29(1): 58-74. [Available Online]
Given the increasing quantity and impressive placement of work on Bayesian process tracing, this approach has quickly become a frontier of qualitative research methods. Moreover, it has dominated the process-tracing modules at IQMR and APSA meetings for over five years, rendering its impact even greater. Proponents of qualitative Bayesianism make a series of strong claims about its contributions and scope of inferential validity. Four claims stand out: (1) it enables causal inference from iterative research, (2) the sequence in which we evaluate evidence is irrelevant to inference, (3) it enables scholars to fully engage rival explanations, and (4) it prevents ad-hoc hypothesizing and confirmation bias. Notwithstanding the stakes of these claims and breadth of traction this method has received, no one has systematically evaluated the promises, trade-offs, and limitations that accompany Bayesian process tracing. This article evaluates the extent to which the method lives up to the mission. Despite offering a useful framework for conducting iterative research, the current state of the method introduces more bias than it corrects for on numerous dimensions. The article concludes with an examination of the opportunity costs of learning Bayesian process tracing and a set of recommendations about how to push the field forward.
Zaks, Sherry. 2021. "Return to the Scene of the Crime: Revisiting Process Tracing, Bayesianism, and Murder."
Political Analysis. 29(1): 306-310. [Available Online via FirstView]
Abstract: Studies on rebel-to-party transition suggest that incorporating former- rebels into post-conflict politics creates a tenable path toward stability and democratization. Notwithstanding the salience of these results, the rebel-to-party literature is racked with an unacknowledged conceptual tension that simultaneously demands—and paves the way for—reconciliation. On the one hand, scholars exhibit remarkable convergence on both the core meaning and stakes of rebel-to-party transition. On the other hand, the literature reveals nearly as many different definitions of rebel-to-party transition as there are studies of it. Conceptual imprecision and discord together have an analytic ripple effect—compromising the validity of the concept, the quality of the measure, the inclusion criteria of datasets, and the results of analyses. To address these limitations, I propose a novel conceptualization of rebel-to-party transition that distinguishes among (failed) political aspirants, nominal participants, and seated participants. This framework places critical scope conditions on “failure,” adds nuance to “success,” and explicitly distinguishes between transition and transformation. I derive frameworks for data collection and measurement and run a series of replication analyses to both test the implications of existing disparities and demonstrate the utility of the new framework.