Digital Repression for Authoritarian Evolution in Saudi Arabia
Bibliographic record
Abstract
Since 2017, Crown Prince Mohamed bin Salman (MbS) has moved to consolidate absolute power in Saudi Arabia, dismantling a system of princely consensus that had defined Al Saud authoritarianism since the assassination of King Faisal in 1975. With a firm grip on the intelligence and security services, MbS has done away with previous norms that allowed some space for dissent in elite circles while heavily repressing most Saudis. The new de-facto leader exhibits acute intolerance of any form of dissent, justified as a necessity for his plans for dramatic social transformation and economic diversification. With this, a series of high-profile cases of domestic and international repression have drawn global attention to the kingdom, not least the murder of Jamal Khashoggi in 2018, the targeting of activists with spyware in Canada and Europe, and the mobilizing of Twitter botnets to create the illusion of popular regime support. Common to these activities is a range of new technological tools and strategies deployed to monitor, target, and deter political dissent. This chapter surveys these new authoritarian practices in Saudi Arabia, contextualizing the micro-practices of repression in a historical framework and through a theoretical lens of civil society depoliticization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".