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Record W2891648408

Authoritarian Practices in the Digital Age| The Contestation and Shaping of Cyber Norms Through China’s Internet Sovereignty Agenda

2018· article· en· W2891648408 on OpenAlexaff
Sarah McKune, Shazeda Ahmed

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCybersecurity and Cyber Warfare Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSovereigntyThe InternetPolitical scienceInternet governanceTransparency (behavior)AuthoritarianismHuman rightsChinaGlobal governanceCorporate governancePolitical economySociologyPublic administrationLawDemocracyBusinessPolitics
DOInot available

Abstract

fetched live from OpenAlex

This article focuses on China as the state dedicating the most coordinated, strategic, and consistent efforts to promoting an Internet sovereignty agenda at home and abroad. At its core, the Chinese case for Internet sovereignty envisions the regime’s absolute control over the digital experience of its population, with a focus on three dimensions: Internet governance, national defense, and internal influence. Through its guidance of the Shanghai Cooperation Organization and creation of the World Internet Conference, normative collaborations with Russia and other states, and promotion of Internet sovereignty as benefiting developing states in particular, the Chinese government is advocating for global recognition of the norm over the long term. Yet growing international support for Internet sovereignty could undermine multistakeholderism, transparency, accountability, and human rights, sparking new flash points in ongoing contestation over digital norms.

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)0.005
metaresearch head score (Gemma)0.003
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 score0.024
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.018
Scholarly communication0.0070.004
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.372
GPT teacher head0.567
Teacher spread0.195 · 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

Citations14
Published2018
Admission routes1
Has abstractyes

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