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Record W2326204440 · doi:10.1177/1461444816639976

Tor, what is it good for? Political repression and the use of online anonymity-granting technologies

2016· article· en· W2326204440 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNew Media & Society · 2016
Typearticle
Languageen
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsCentre for International Governance Innovation
Fundersnot available
KeywordsAnonymityPoliticsThe InternetGovernment (linguistics)Internet privacyCivil societyPolitical scienceSociologyLaw and economicsPublic relationsLawComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Why do people use anonymity-granting technologies when surfing the Internet? Anecdotal evidence suggests that people often resort to using online anonymity services, like the Tor network, because they are concerned about the possibility of their government infringing their civil and political rights, especially in highly repressive regimes. This claim has yet to be subject to rigorous cross-national, over time testing. In this article, econometric analysis of newly compiled data on Tor network usage from 2011 to 2013 shows that the relationship between political repression and the use of the Tor network is U-shaped. Political repression drives usage of Tor the most in both highly repressive and highly liberal contexts. The shape of this relationship plausibly emerges as a function of people’s opportunity to use Tor and their need to use anonymity-granting technologies to express their basic political rights in highly repressive regimes.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.273
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it