In the Heart of Liberal Democracy: Whitewashing Authoritarian Surveillance a Decade After the Snowden Revelations
Bibliographic record
Abstract
Practices of authoritarian surveillance are not limited to so-called authoritarian states. Authoritarian surveillance has been increasingly witnessed in countries that uphold liberal democratic principles and institutions in the twenty-first century, along with accelerated automation of mass surveillance. This article situates authoritarian surveillance as a problem in the heart of democracy and capitalism in its materiality and imperial expansion and discusses how mass surveillance has redrawn boundaries of democratic principles. What Edward Snowden has revealed since 2013 is a good reminder of the materiality of the global surveillance networks built under the “War on Terror” that we still live in. They are embedded within digital communication infrastructures by the security agencies in the United States, the self-defined champion of liberal democracy. Nonetheless, a decade after the Snowden revelations, the authoritarian surveillance within democracy has been whitewashed in technological competitions with the Global South, such as China and India, and in tech-savvy culture in the Global North. What is the driving force of this oblivion? In a contrast with the COVID-19 pandemic surveillance, I highlight the exceptional position given to the security intelligence and policing agencies within liberal democratic institutions to generate authoritarian surveillance continuously from the imperial to postcolonial times. It is critically important to deconstruct the binary discourse of democratic states versus authoritarian states because this view tends to only problematize surveillance activities by countries located outside the “West” and sees authoritarian surveillance practiced by self-defined democratic states as harmless or even necessary to counter perceived authoritarian states. It further fuels global dissemination of surveillance technologies, just like the nuclear arms races, and produces more victims of authoritarian surveillance on earth, without borders.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".