Diffusion without Isomorphism: Transnational Interstitial Spaces and the Official Categorization of Blackness in Mexico, 1994–2015

• In Event: The Racial State

Abstract

In the past four decades, a global template for enumerating people of African descent has taken shape. Most Latin American states now include Afrodescendent populations in their censuses, yet they do so using strikingly different categories. This pattern poses a puzzle: how did states converge in adopting ethnoracial census questions while diverging so markedly in their categorical schemes? Institutional theory predicts convergence in both form and content. Fieldtheoretic approaches foreground domestic classification struggles but treat global dynamics as exogenous context. Neither framework accounts for the simultaneous convergence and divergence that characterizes this process. This article examines Mexico’s 2015 Intercensal Survey, the first official instrument to enumerate Black people in this country’s history, to explain how diffusion without isomorphism unfolds. I argue that heterogeneous diffusion is produced through diagonal differentiation across field scales. This process generates transnational interstitial spaces in which actors with competing forms of authority engage in scalar mediation to define official categories. Drawing on twelve months of fieldwork, 36 interviews, 180 documentary sources, and correspondence analysis of over 1,600 coded category-actor pairings, the article identifies three enabling conditions for this process. Conjunctural conditions open the interstitial space through critical junctures. Structural conditions configure the space through multi-level transposition of categories. Pragmatic conditions transform competing categorical stances into official state representation through bureaucratic appropriation. The analysis contributes to scholarship on transnational fields, knowledge production, and the politics of classification by specifying how globally circulating templates produce locally divergent knowledge outcomes.

Author

• Juan D. Delgado, University of Michigan-Ann Arbor