Bibliographic record
Abstract
Abstract Remote-access cyber espionage operations against activists, dissidents or human rights defenders abroad are increasingly a feature of digital transnational repression. This arises when State or State-related actors use digital technologies to silence or stifle dissent from human rights defenders, activists and dissidents abroad through the collection of confidential information that is then weaponized against the target or their networks. Examples include the targeting of Ghanem Al-Masarir (a Saudi dissident living in the United Kingdom), Carine Kanimba (a United States–Belgian dual citizen and daughter of Rwandan activist Paul Rusesabagina living in the United States) and Omar Abdulaziz (another Saudi dissident living in Canada) with NSO Group's mercenary spyware. This practice erodes human rights, democracy and the rule of law and has a negative impact on targeted communities, including social isolation, self-censorship, the fragmentation and impairment of transnational political and social advocacy networks, and psychological and social harm. Despite this, international law does little to restrain this practice. Building on momentum around the regulation of mercenary spyware and transnational repression, this article elaborates on how States could consider regulating dissident cyber espionage and streamlines a unified approach among ratifying States addressing issues such as State immunity, burden of proof, export control and international and public–private sector collaboration.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".