Beyond Big Brother: How to Study Tech-Driven Authoritarianism With Restricted Access to State Institutions
Bibliographic record
Abstract
With the tremendous advancements in Internet, big data analytics, and artificial intelligence, the power and potential of digital technologies has a special appeal to political rulers. How can qualitative researchers explore tech-driven authoritarianism when they have limited access to state institutions? This article addresses this question by arguing for a wider and more nuanced understanding of tech-driven authoritarianism as a state-market complex mediating the political application of digital technologies. Based on my own research on China’s Internet surveillance, I find that the engagement of the private sector, especially technology companies, in authoritarian control creates new opportunities for qualitative researchers to study state power in non-state fields. By reflecting on my experience of field-site choice, gaining access, and informant recruitment, I discuss how thorough preparation in both theory and fieldwork approaches help qualitative investigators develop creative ways of collecting information on tech-driven authoritarianism.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".