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Record W4380481959 · doi:10.6000/1929-4409.2020.09.256

International Problems in the Information Policy of Russian Digital Media

2022· article· en· W4380481959 on OpenAlexvenueno aff
Tatyana Anatolievna Nagovitsina, Ramis R. Gazizov

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2022
Typearticle
Languageen
FieldComputer Science
TopicScientific Research and Philosophical Inquiry
Canadian institutionsnot available
FundersKazan Federal University
KeywordsGlobalizationPanoramaMass mediaPolitical scienceWhite (mutation)Public relationsMedia studiesSociologyLawComputer science

Abstract

fetched live from OpenAlex

The article is devoted to the study of international issues in leading digital media in Russia. This topic has been and remains a priority on the media agenda of the country. Given the increasing globalization and increasing international factors, the mass audience needs timely and accessible information in this area. When analyzing the materials, attention was paid to the problems, substantive characteristics, structural organization, genre specifics, linguistic and stylistic aspects, which, interacting, form a diverse panorama of international information covered by publications. The information policy of such publications as Lenta.ru, Meduza, Vzglyad and White Square Journal is investigated.

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)0.015
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0060.007
Scholarly communication0.0270.014
Open science0.0010.005
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.0090.002

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 head0.091
GPT teacher head0.349
Teacher spread0.257 · 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 designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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