Digital Transnational Repression and Host States’ Obligation to Protect Against Human Rights Abuses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it