The Dictator's Digital Toolkit: Explaining Variation in Internet Filtering in Authoritarian Regimes
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
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
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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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