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Record W4312095849 · doi:10.37016/mr-2020-110

Mapping the website and mobile app audiences of Russia’s foreign communication outlets, RT and Sputnik, across 21 countries

2022· article· en· W4312095849 on OpenAlexaboutno aff
Julia Kling, Florian Toepfl, Neil Thurman, Richard Fletcher

Bibliographic record

VenueHarvard Kennedy School Misinformation Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Influence and Politics
Canadian institutionsnot available
FundersHORIZON EUROPE European Research CouncilEuropean Commission
KeywordsQuarter (Canadian coin)Mobile appsPolitical scienceAdvertisingGeographyBusinessWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

Following Russia’s invasion of Ukraine, policymakers worldwide have taken measures to curb the reach of Russia’s foreign communication outlets, RT and Sputnik. Mapping the audiences of these outlets in 21 countries, we show that in the quarter before the invasion, at least via their official websites and mobile apps, neither outlet reached more than 5% of the digital populations of any of these countries each month. Averaged across all countries, both outlets’ website and mobile app reach remained approximately constant between 2019 and 2021, was higher for men, and increased with audiences’ age.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.027
GPT teacher head0.310
Teacher spread0.283 · 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 designObservational
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

Citations12
Published2022
Admission routes1
Has abstractyes

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