"The Myth of the Zero Sum: Rethinking Acculturation in American Politics" (under review, Politics, Groups, and Identities) 

Political science has long assumed that acculturation operates as a zero-sum tradeoff. Using the 2006 Latino National Survey, I conduct the first direct empirical test and find that binary models fail to capture the orientations of over 75% of Latino respondents. Comparative cluster analysis reveals four distinct acculturation orientations, with three algorithms converging on the same structure, demonstrating that the dominant framework in political science structurally misclassifies the vast majority of Latino voters.

Replication materials and code are available at: https://github.com/jagrijalva/bam-scale 

"Hidden in Plain Sight: Latino Political Diversity Before the Trump Era" (under review, Political Psychology) 

Binary acculturation models recognize only two orientations and misclassify 76% of Latino voters. Using the Bidimensional Acculturation Model and the 2006 Latino National Survey, I identify four distinct orientations among 4,785 eligible voters. Each produces a distinct political profile, with immigration attitudes showing the strongest associations, significantly exceeding ideology and partisanship. Latino political diversity is not puzzling variation but the predictable consequence of different strategies for managing the cross-pressures of navigating American society as members of a racialized immigrant-origin population. 

Replication materials and code are available at:
https://github.com/jagrijalva/acculturation-politics

"Stable Drivers of Campaign Effects: Mapping Latino Support for Trump" (conference paper, MPSA 2026; working paper)

What explains Latino support for Donald Trump? Using data from the Collaborative Multiracial Post-Election Survey (CMPS) in 2016 and 2020, this paper employs random forest classification with SHAP value interpretation to identify and rank competing predictors of Latino Trump support. The method captures non-linear relationships and complex interactions without pre-specification. I estimate models with and without partisanship to distinguish proximate from underlying drivers, finding that immigration attitudes emerge as the most prominent predictor beyond partisanship, and that their importance does not diminish when partisan identification is removed from the model.
 
Replication materials and code for the 2016 analysis are available at: https://github.com/jagrijalva/ml-latino-vote-2016

"Democracy for Whom? Herrenvolk Origins and the Design of American Exclusion" (under review, PS: Political Science & Politics)

This paper argues that the United States was founded as a herrenvolk democracy, a system granting democratic rights to the herrenvolk, or "master race," while excluding and dominating other groups. By tracing key decisions from the early colonial period through the Declaration and Constitution, and culminating in the 1790 Naturalization Act, which restricted citizenship to “free white persons,” I show that this exclusionary herrenvolk foundation was an intentional choice, consistently upheld against more inclusive visions. On the nation’s 250th anniversary, as the project of a genuine multiracial democracy remains unfulfilled, understanding this founding intent is critical to confronting its legacy.

"The Power-Sharing Index: Measuring Democracy Beyond Institutions" (preparing for submission to V-Dem Working Paper Series, subsequent submission to Journal of Democracy) 

Standard democracy indices measure whether democratic procedures function; they cannot detect whether those procedures function for everyone. This paper introduces the Power-Sharing Index (PSI), a measure of cross-group power distribution built from five V-Dem indicators. Validation demonstrates excellent internal consistency (Cronbach's α = 0.96) and robust construct validity: the index responds appropriately to known historical events. The critical discriminant validity test reveals what I call the "Herrenvolk Paradox": during 1789–1899, PSI shows a negative correlation with V-Dem's Electoral Democracy Index (r = −0.25), demonstrating that procedural democracy expanded among white men while cross-group power-sharing remained near zero. 

Replication materials and code are available at: https://github.com/jagrijalva/psi-scale

Book Project: The Herrenvolk State: Power and Exclusion in American Democracy (under consideration with Cambridge University Press and Princeton University Press) 

This book argues that American democracy was designed as a herrenvolk democracy and that the current crisis is not a departure from a healthy democratic past but the latest manifestation of this enduring regime type. I develop a novel Power-Sharing Index that captures variation in whether and how non-white groups have been systematically excluded throughout American history. The index reveals four distinct eras in American political development: the Herrenvolk Era (1776–1863), Reconstruction (1863–1877), Partial Inclusion (1877–1965), and the Power-Sharing Dilemma (1965–present). The book traces how citizenship and immigration law operated as primary mechanisms of exclusion and power-sharing from the 1790 Naturalization Act to the contemporary democratic crisis.
 
Replication materials and code are available at:
https://github.com/jagrijalva/psi-scale

"Building Inference in Cluster Analysis with Multi-Algorithm Validation" (working paper) 

Cluster analysis is widely used in political science to discover latent structure, yet it is typically dismissed as "merely exploratory." This paper transforms cluster analysis into a rigorous framework for hypothesis testing. The core insight is that different clustering algorithms encode conflicting assumptions about data geometry, and when algorithms with divergent foundations converge on the same solution, that convergence constitutes evidence that recovered patterns reflect genuine data features rather than methodological artifacts. An application to competing models of acculturation demonstrates the framework's capacity: five algorithms were tested, three converged on a four-cluster solution that falsified the dominant binary model in political science.
 
Replication materials and code are available at: https://github.com/jagrijalva/bam-scale

"Interpretable Machine Learning for Political Behavior Research" (conference paper, AAPOR 2026; working paper)

As of late 2025, no published article in a core political science journal uses random forest with SHAP for individual-level survey analysis of political behavior. This paper provides a replicable methodological framework for doing so. I address three limitations of traditional regression: SHAP values capture non-linear effects and high-order interactions without manual specification, provide both global feature importance and individual-level explanations, and enable principled model comparison across time periods or subgroups. I provide a practical guide for implementation in R, discuss best practices for variable selection, and illustrate how to interpret SHAP summary plots for substantive inference. The application uses Latino support for Trump as a case study, drawing on CMPS data from 2016 and 2020. 

Replication materials and code for the 2016 analysis are available at: https://github.com/jagrijalva/ml-latino-vote-2016

Power-Sharing Index (PSI): Cross-group power distribution measure built from V-Dem indicators. https://github.com/jagrijalva/psi-scale 

Bidimensional Acculturation Model (BAM): Multi-algorithm cluster analysis and scale construction. https://github.com/jagrijalva/bam-scale 

ML Latino Vote: Random forest + SHAP analysis of Latino vote choice (2016, 2020, 2024). https://github.com/jagrijalva/ml-latino-vote-2016 

Acculturation & Politics: Cross-pressures analysis linking acculturation orientations to political behavior. https://github.com/jagrijalva/acculturation-politics