Hostname: page-component-77f85d65b8-2tv5m Total loading time: 0 Render date: 2026-03-28T11:45:13.292Z Has data issue: false hasContentIssue false

Racial Tropes in the Foreign Policy Bureaucracy: A Computational Text Analysis

Published online by Cambridge University Press:  02 August 2024

Austin Carson*
Affiliation:
Department of Political Science, University of Chicago, IL, USA
Eric Min
Affiliation:
Department of Political Science, University of California, Los Angeles, USA
Maya Van Nuys
Affiliation:
Department of Political Science, University of Chicago, IL, USA
*
*Corresponding author. Email: acarson@uchicago.edu

Abstract

How do racial stereotypes affect perceptions in foreign policy? Race and racism as topics have long been marginalized in the study of international relations but are receiving renewed attention. In this article we assess the role of implicit racial bias in internal, originally classified assessments by the US foreign policy bureaucracy during the Cold War. We use a combination of dictionary-based and supervised machine learning techniques to identify the presence of four racial tropes in a unique corpus of intelligence documents: almost 5,000 President's Daily Briefs given to Kennedy, Johnson, Nixon, and Ford. We argue and find that entries about countries that the US deemed “racialized Others”—specifically, countries in the Global South, newly independent states, and some specific regional groupings—feature an especially large number of racial tropes. Entries about foreign developments in these places are more likely to feature interpretations that infantilize, invoke animal-based analogies, or imply irrationality or belligerence. This association holds even when accounting for the presence of conflict, the regime type of the country being analyzed, the invocation of leaders, and the topics being discussed. The article makes two primary contributions. First, it adds to the revival of attention to race but gives special emphasis to implicit racialized thinking and its appearance in bureaucratic settings. Second, we show the promise of new tools for identifying racial and other forms of implicit bias in foreign policy texts.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press on behalf of The IO Foundation
Figure 0

TABLE 1. Original terms used with thesaurus to create dictionaries

Figure 1

TABLE 2. Performance of the highest-quality predictive models

Figure 2

TABLE 3. President's Daily Brief entries with high predicted probability of exhibiting key tropes

Figure 3

FIGURE 1. Histograms of dependent variables

Figure 4

FIGURE 2. Depictions of explanatory variables

Figure 5

TABLE 4. Results of regressions on relationship between racial tropes in PDB entries and measurements of racialized Otherness

Figure 6

TABLE 5. Predicted values from models in Table 4

Figure 7

FIGURE 3. Coefficient plots of regressions in Table 4 and additional models that include topic-prevalence measures from the structural topic model

Figure 8

FIGURE 4. Geographical regions used in the analysis

Figure 9

FIGURE 5. Coefficient plots of regressions disaggregating countries by region

Figure 10

FIGURE 6. Coefficient estimates for the global south variable, using a moving seven-year window and full models accounting for topics

Supplementary material: File

Carson et al. supplementary material

Carson et al. supplementary material
Download Carson et al. supplementary material(File)
File 418.1 KB
Supplementary material: Link

Carson et al. Dataset

Link