Autocracy's long reach: explaining host country influences on transnational repression
Bibliographic record
Abstract
Authoritarian regimes frequently reach across borders to repress against exiled dissidents.Existing scholarship has investigated the methods and effects of transnational repression.Yet, we lack knowledge of the role that the political context of a host country and its relations to the origin country of diasporas play in incidents of transnational repression.Addressing this gap, we use a Freedom House dataset on physical acts of transnational repression (2014-2020) to study how the regime type of the host country and the regional ties between the host and origin country influence the likelihood and type of transnational repression incidents.Conducting a logistic regression analysis with yearly directed dyads, we find that to target exiles in autocratic host states perpetrators primarily rely on the cooperation of authorities, whereas in democratic host states they resort more often to direct attacks.We also show that authoritarian cooperation on transnational repression is regionally clustered: it often occurs when home and host state are situated within the same authoritarian neighbourhood, and partly also when they are members in the same regional organization.Our article reveals some of the host state conditions and relational dynamics that shape the decisions and strategies of transnational repression perpetrators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".