Censorship and the Impact of Repression on Dissent
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 What is the impact of repression on opposition to authoritarian rule? Studies of repression and dissent have yielded contradictory results. Some research suggests that repression reduces popular resistance while others show that it creates backlash and more dissent. In this article, we present an informational theory of repression to account for such divergent findings. We argue that the impact of repression hinges on the degree of censorship. Where alternative media is present, violence is more likely to increase support for opposition. By contrast, where alternative sources of information are limited, repression may reduce support for opposition and actually increase support for incumbents. We test and confirm these expectations with an original dataset that combines the results of a panel survey that spanned the authoritarian repression of electoral protests in Moldova in 2009 and geocoded data on the subnational variation in repression and alternative information availability. The hypothesized interaction between repression and censorship is corroborated in cross‐national analysis of repression, censorship, and government support (2005–16).
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.010 |
| Scholarly communication | 0.000 | 0.000 |
| 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