The Myth of the Zero-Sum: Rethinking Acculturation in American Politics

06 March 2026, Version 1
This content is an early or alternative research output and has not been peer-reviewed at the time of posting.

Abstract

Acculturation theory has long assumed a zero-sum tradeoff where stronger attachment to a dominant culture means weaker attachment to a heritage culture. This foundational premise has never been empirically tested, yet it governs how political science measures and understands political incorporation. Using comparative cluster analysis across multiple algorithms with extensive validation procedures, this paper provides the first rigorous test of the binary model against a bidirectional alternative, drawing on the 2006 Latino National Survey (N = 4,785 eligible voters). The binary model fails. Four distinct acculturation orientations emerge — bicultural (68.4%), assimilationist (14.7%), culture-affirming (9.0%), and demicultural (7.9%) — and hybrid orientations constitute over three-quarters of the sample. The zero-sum framework structurally excludes the majority it claims to explain. These findings challenge foundational assumptions about acculturation theory and political incorporation, and demonstrate how unsupervised machine learning with rigorous validation enables hypothesis testing of theoretical frameworks long treated as settled.

Supplementary materials

Title
Description
Actions
Title
Appendices
Description
The supplementary appendices provide full methodological documentation for the analyses reported in the manuscript. Appendix A presents a systematic review of 23 acculturation studies in political science (1994–2020), documenting universal reliance on proxy measures and conflation of acculturation with assimilation. Appendix B examines measurement limitations across major Latino political surveys, demonstrating that only the 2006 Latino National Survey captures the independent identity strength measures required to detect hybrid orientations. Appendix C reports full sample characteristics and survey item wording. Appendix D provides complete technical documentation of the cluster analysis methodology, including model selection criteria, validation results across five algorithms, and cluster differentiation statistics.
Actions

Supplementary weblinks

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.