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Record W3098728960 · doi:10.5539/jpl.v13n4p110

Social– Political Integrity in the 21st Century: Threats and Risks of the Digitalization

2020· article· en· W3098728960 on OpenAlexvenueno aff
Mamychev Alexey Yurievich, Alexander Kim, Dremliuga Roman Igorevich, Surzhik Mariia, Zheng Fuxue

Bibliographic record

VenueJournal of Politics and Law · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSecurity, Politics, and Digital Transformation
Canadian institutionsnot available
FundersRussian Foundation for Basic Research
KeywordsPoliticsSustainabilityReproductionState (computer science)Convergence (economics)Political sciencePosition (finance)Environmental ethicsSociologyPolitical economyLawBusinessEconomic growthEconomicsComputer science

Abstract

fetched live from OpenAlex

Socio-political problems are discussed in this article connected with the provision of socio-cultural integrity of the society in modern time of mass digitalization and introduction of the automatic and algorithmic systems. In the content of this article digitalization is considered as a global socio-political project, oriented for substitution traditional bases of identification and organization communities. This project is considered from critical position and is based, that its necessary state – oriented policy, directed for conservation and reproduction the historical memory, socio-cultural dominant of the development the society and also metapolitical and meta-legal foundation for sustainability of the political – legal organization in the 21st century. The authors speak about the thesis about further convergence of the digital and cultural trends of transformations of socio-political system, when processes of digitalization will acquire more and more socio-cultural particularity of the development.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.028
Scholarly communication0.0150.011
Open science0.0010.010
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.114
GPT teacher head0.375
Teacher spread0.262 · 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 designTheoretical or conceptual
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

Citations1
Published2020
Admission routes1
Has abstractyes

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