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China Watchers

Published online by Cambridge University Press:  07 November 2025

Franziska B. Keller
Affiliation:
University of Bern , Switzerland
Johan A. Dornschneider-Elkink
Affiliation:
University College Dublin , Ireland
Hans H. Tung
Affiliation:
National Taiwan University , Taiwan
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Abstract

Recent debates have raised concerns about how academia and policy makers alike rely on country experts when trying to understand the politics of authoritarian regimes. This study argues that one possible source of bias is the experts’ network of affiliations and interactions with one another and that we therefore should make the social background and networks of country experts more transparent. We implemented this by examining experts on contemporary Chinese politics using a nomination process to establish a list of 2,200 such experts. We find that US-based and US-educated male academics continue to form the core of this community but that younger cohorts appear to be more diverse in terms of educational background, gender, and geographic location. Our findings provide not only the first analysis of the global China Watcher community but also speak to current debates about the reliability of aggregated expert assessments.

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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 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of American Political Science Association
Figure 0

Figure 1 Distribution of Gender (Right), Type of Work Institution (Middle), and Geographic Region (Left) of China Watchers in our DataNumbers indicate the number of respondents in each category and percentages indicate the response rate in that category.

Figure 1

Figure 2 Educational Background of China Watchers in our DatabaseTop 10 universities (white) and distribution among institutions mentioned once (dark gray) or two to 12 times in the dataset (light gray). The length of the bar section is proportional to the number of China Watchers attending the specific institution(s).

Figure 2

Figure 3 Gender Distribution of Different China Watcher Cohorts

Figure 3

Figure 4 Diversity in the Educational Background (Undergraduate, Postgraduate, and PhD-Degree Institution) of China Watcher CohortsDiversity is measured using the Gini coefficient: if all members of the cohort attended the same institution, the coefficient is 1; if everyone went to a different institution, the coefficient is 0.

Figure 4

Figure 5 Fraction of China Watchers With Degree from an Institution in Mainland China, by Cohort

Figure 5

Figure 6 The Nomination Network of the China Watchers in our DatabaseThe size of the node is proportional to indegree (i.e., the number of nominations), the color represents the geographical region of the work institution, and letters indicate the type of institution (i.e., a=academic, g=government, i=independent, n=news, N=NGO, p=private company, and t=think tank). Layout algorithm: Force Atlas 2 as implemented by Gephi.

Figure 6

Figure 7 Network of Experts in Geographical Region (Left) or Type of Institution (Right) Nominating One AnotherThe size of the nodes is proportional to the number of experts; the width of the arcs is proportional to the number of nominations; the color of the arcs corresponds to the sending node; and the arcs follow a clockwise direction. Layout algorithm: Force Atlas 2 as implemented by Gephi.

Figure 7

Figure 8 Nomination Network of US-Based China WatchersColors indicate whether the expert signed the Washington Post (dove) or the Journal of Political Risk (hawk) open letter or nominated an expert who did. The size of the node is proportional to the number of nominations overall. Layout algorithm: Force Atlas 2 as implemented in Gephi.

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