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Digital Democracy in Authoritarian Russia

2017· book-chapter· en· W2594293680 on OpenAlexaff
Rachel Baarda

Bibliographic record

VenueAdvances in electronic government, digital divide, and regional development book series · 2017
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAuthoritarianismDemocracyPolitical scienceEconomic systemControl (management)Political economySociologyEconomicsLawPoliticsManagement

Abstract

fetched live from OpenAlex

Digital media is expected to promote political participation in government. Around the world, from the United States to Europe, governments have been implementing e-government (use of of the Internet to make bureaucracy more efficient) and promising e-democracy (increased political participation by citizens). Does digital media enable citizens to participate more easily in government, or can authoritarian governments interfere with citizens' ability to speak freely and obtain information? This study of digital media in Russia will show that while digital media can be used by Russian citizens to gain information and express opinions, Kremlin ownership of print media, along with censorship laws and Internet surveillance, can stifle the growth of digital democracy. Though digital media appears to hold promise for increasing citizen participation, this study will show that greater consideration needs to be given to the power of authoritarian governments to suppress civic discourse on the Internet.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0050.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.013
GPT teacher head0.262
Teacher spread0.249 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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