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Record W2787435073 · doi:10.1177/1367549417751151

Branding Internet sovereignty: Digital media and the Chinese–Russian cyberalliance

2018· article· en· W2787435073 on OpenAlexaff
Stanislav Budnitsky, Lianrui Jia

Bibliographic record

VenueEuropean Journal of Cultural Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCybersecurity and Cyber Warfare Studies
Canadian institutionsYork UniversityCarleton University
Fundersnot available
KeywordsSovereigntyThe InternetInternet governancePolitical scienceChinaNation brandingOpposition (politics)HegemonyDigital mediaPoliticsContext (archaeology)Political economyMedia studiesEconomySociologyPublic relationsLawEconomics

Abstract

fetched live from OpenAlex

In the 2000s, China and Russia emerged as outspoken actors with global ambitions. To communicate their status aspirations, both countries introduced a range of nation-branding institutions and initiatives. Global Internet governance – the design and administration of Internet technology and related policymaking – is among the domains where China and Russia have asserted their national brands. The Chinese and Russian governments co-advance the brand narrative of ‘Internet sovereignty’ in opposition to perceived technological and governance hegemony of the United States. Given the power that private online intermediaries wield in the political economy of the Internet, national digital media champions, China’s Baidu and Russia’s Yandex, have been integral to their countries’ Internet branding efforts. The article examines how China and Russia have forged a public–private relationship with respective digital media champions in the context of building and branding an Internet sovereignty agenda.

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: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.049

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 studies0.0040.010
Scholarly communication0.0050.004
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.308
Teacher spread0.276 · 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

Citations127
Published2018
Admission routes1
Has abstractyes

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