Gender-based digital transnational repression and the authoritarian targeting of women in the diaspora
Bibliographic record
Abstract
Authoritarian governments rely on digital transnational repression to silence criticism and dissent outside their territories. Women human rights defenders and journalists in exile and in the diaspora face particular, gendered forms of digital threats that exploit their gender identity to intimidate, shame and discredit them. Based on 51 qualitative interviews with women human rights defenders originating from six countries (Azerbaijan, Eritrea, Iran, Russia, Turkey, Xinjiang/China) and residing in 17 host countries, this article investigates some key tactics of gender-based digital transnational repression. We argue that monitoring, invasive surveillance, online harassment and defamation aim to either subject the exiled women activists to the control of the repressive state again or stigmatize and distance them from their communities. By turning misogyny into a tool of political repression, authoritarian regimes are able to amplify digital attacks against exiled women activists, expanding the actor configurations involved their repressive strategies. Our findings highlight how gender-based digital transnational repression extends forms of authoritarian domination from domestic and “offline” settings into digital space and the transnational field.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| 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".