First, the Bad News: Opposition Media in Authoritarian Regimes
Bibliographic record
Abstract
Why do dictatorships sometimes allow opposition groups to publish media, but at other times forbid opposition media? I propose a theory that dictators tolerate opposition media selectively in order to limit protests. I formalize the theory in a signaling model, from which I derive several testable empirical implications. I illustrate the logic of the theory with a case study of the Ben Ali dictatorship in Tunisia during its first five years (1987-1992), based in part on interviews I carried out in Tunisia. I show how the theory explains variation in Ben Ali's willingness to allow opposition media, across both time and opposition groups. To test the implications of the model quantitatively, I construct a panel dataset on ten Arab countries with authoritarian regimes during 1992-2017. The data measure which regimes allowed opposition groups to produce media in which years and are based on my research on a wide range of opposition groups (of various ideologies and legal statuses) and of media (including newspapers, websites, and TV channels).I find that dictators allow opposition media when their regimes are most likely to survive an uprising, in order to signal their strength to citizens and discourage them from protesting. In particular, in years when authoritarian regimes experience strong economic performance – including low unemployment, high economic growth, and plentiful revenue from oil and natural gas – they are far more likely to permit opposition media. After the "Arab Spring" uprisings of 2011 revealed that the region's authoritarian regimes were more vulnerable to mass unrest than they previously appeared, those regimes became much less likely to tolerate opposition media. By advancing a new theory and analyzing original empirical evidence, this study contributes to our understanding of why media freedom varies in authoritarian regimes.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".