Allan Colbern, Arizona State University
Shawn Walker, Arizona State University
The news plays a crucial yet complex role in shaping public discourse — providing background on important events and deciding who the experts are. Given reporters reliance on multiple sources and competing sides, scholars are faced with the challenge of revealing how and why competing news frames emerge in the news’ coverage of immigration politics and (im)migrants, and the consequences of these frames for policy developments. We argue that the kinds of entities that reporters choose to name/use as experts is essential to situating the news as a critical institution in immigration federalism. We examine 100,000 news articles from ProQuest and NexusUni databases published since 1980 on the topic of “sanctuary” and immigration enforcement at the state and local levels. To capture expertise in the news, we train and develop a custom classifier using machine learning techniques to identify whether a named entity is referenced by reporters as an expert (e.g., quoted) or simply as background context. We then typologize and analyze specific entity types (pro- and anti-immigrant organizations, federal and local law enforcement, Democratic and Republican elected officials, and policy area experts) for how they frame immigration enforcement issues and policies, using a combination of qualitative coding and corpus linguistics. Going beyond pure description, we conduct a cross sectional time series to examine the relation between entity-frames and subnational policy developments relating to sanctuary and enforcement. Our article bridges American political development and immigration federalism with multiple fields, from critical data studies, information science, communication, journalism, to REP and critical migration studies, where the study of framing and language have a much stronger theoretical and empirical foundation.
No extended abstract or paper available
Presented in Session 228. Migration Policy and American Political Development