Digital Transnational Repression and Host States’ Obligation to Protect Against Human Rights Abuses
Bibliographic record
Abstract
Abstract In October 2018, public research by the Citizen Lab, a research laboratory at the Munk School of Global Affairs and Public Policy at the University of Toronto, documented how a Saudi dissident living in Montreal, Canada, was likely targeted with spyware operated by the Saudi authorities. The target, Omar Abdulaziz, was a close friend of murdered journalist Jamal Khashoggi. Both individuals were the object of the increasingly ‘long-arm’ of repressive regimes. These were not isolated incidents, but part of a broader pattern of state repression. This article considers the digital dynamics of the phenomenon of transnational repression in more detail. Specifically, it looks at how states that host targeted dissidents and activists (‘host states’) are responding (or not) to the use of digital technologies to silence transnational political and social debate and dissent. It argues that host states which are parties to international human rights instruments such as the International Covenant on Civil and Political Rights must act in conformity with their positive obligations under international human rights law and suggests a baseline of ‘good practices’ that should be considered by host states in addressing digital transnational repression.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".