The Dictator's Digital Toolkit: Explaining Variation in Internet Filtering in Authoritarian Regimes
Bibliographic record
Abstract
Following its global diffusion during the last decade, the Internet was expected to become a liberation technology and a threat for autocratic regimes by facilitating collective action. Recently, however, autocratic regimes took control of the Internet and filter online content. Building on the literature concerning the political economy of repression, this article argues that regime characteristics, economic conditions, and conflict in bordering states account for variation in Internet filtering levels among autocratic regimes. Using OLS‐regression, the article analyzes the determinants of Internet filtering as measured by the Open Net Initiative in 34 autocratic regimes. The results show that monarchies, regimes with higher levels of social unrest, regime changes in neighboring countries, and less oppositional competition in the political arena are more likely to filter the Internet. The article calls for a systematic data collection to analyze the causal mechanisms and the temporal dynamics of Internet filtering. Related Articles Glen , Carol M. 2014 . “” Politics & Policy 42 (): 635 ‐ 657 . http://onlinelibrary.wiley.com/doi/10.1111/polp.12093/abstract Reynolds , Peter W. 2003 . “.” Politics & Policy 31 (): 512 ‐ 529 . http://onlinelibrary.wiley.com/doi/10.1111/j.1747-1346.2003.tb00160.x/abstract Fisher , Bonnie , Michael Margolis , and David Resnick . 1996 . “.” Southeastern Political Review 24 (): 399 ‐ 429 . http://onlinelibrary.wiley.com/doi/10.1111/j.1747-1346.1996.tb00088.x/abstract Related Media . 2009 . “Dr. Ronald Deibert, Director of the Citizen Lab at Toronto University and the Open Net Initiative Presents a Recently Completed Global Survey of more than 45 countries that Censor Online.” https://www.youtube.com/watch?v = BWMn7RzdIX0 Websites: Website of the Open Net Initiative including the data used in this paper, country reports and online access to further material. https://opennet.net
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".