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Record W2991576223 · doi:10.1163/1871191x-14401070

Russia’s Strategy for Perception Management through Public Diplomacy and Influence Operations: The Canadian Case

2019· article· en· W2991576223 on OpenAlexaffabout
Evan H. Potter

Bibliographic record

VenueThe Hague Journal of Diplomacy · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPublic diplomacyDiplomacyPolitical scienceInternational relationsState (computer science)Public administrationPerceptionNation brandingForeign policyPublic relationsPolitical economySociologyLawPolitics

Abstract

fetched live from OpenAlex

Summary This article argues that official Russian global media platforms such as Russia Today (RT) and Sputnik News, as well as Kremlin-friendly news outlets, represent the overt face of Russia’s global information ecology. The article discusses how such platforms fit into a framework for public diplomacy that has less-restrictive conceptual boundaries, and examines the intersection of public diplomacy with other dimensions of a nation-state’s operations for international influence. The article avers that a broader understanding of Russia’s international communication practices permits the inclusion of so-called ‘sharp’ practices as part of the strategic communications component of public diplomacy. It examines the case study of a Canadian foreign minister’s family history, illustrating Russia’s approach to international perception management through public diplomacy.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.149
Threshold uncertainty score0.988

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0360.015
Scholarly communication0.0100.002
Open science0.0020.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.051
GPT teacher head0.361
Teacher spread0.310 · 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

Citations6
Published2019
Admission routes2
Has abstractyes

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