Sowing Hate, Cultivating Loyalists: Mobilizing Repressive Nationalist Diasporas for Transnational Repression by the People’s Republic of China Regime
Bibliographic record
Abstract
Pundits, advocates, and scholars have increasingly focused on the strategies of transnational repression employed by autocratic states to deter opposition and control the voices of emigrants abroad. Typically, transnational repression is understood as various forms of state-directed tactics executed by institutional actors who are deployed, trained, and organized by the state. Yet, the tactic of inciting hatred and division among emigrants to undermine dissidents—part of a nationalist strategy that mobilizes non-state actors for repression—has not been thoroughly explored. This article reveals a unique form of diaspora actorhood: the repressive nationalist diasporas, which consist of culturally driven migrants who support their authoritarian homelands and exert significant influence in various aspects of transnational migrants’ civic life, including student groups, ethnic associations, and grassroots organizations. Through these networks, diaspora migrants provide autocrats with the means to extend their repressive reach internationally. This paper examines the People’s Republic of China (PRC) as a case study, demonstrating how the state leverages nationalist sentiments to alienate dissidents and fuel enmity among its loyalists overseas. The consequences are extensive, involving surveillance, harassment, and assaults on dissidents such as Hong Kongers, Tibetans, Uyghurs, Taiwanese, and mainland Chinese—aiming to silence those who criticize the PRC regime. Ethnographic research, interviews, and publicly available data are used to reveal, describe, and analyze the role and global reach of the repressive nationalist diaspora in the transnational repression mechanism as part of modern autocratic statecraft.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".