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Record W2013146153 · doi:10.1080/00905992.2010.498471

Stalin: authoritarian populist or great Russian chauvinist?

2010· article· en· W2013146153 on OpenAlexaff
David R. Marples

Bibliographic record

VenueNationalities Papers · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicEastern European Communism and Reforms
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsProletariatAuthoritarianismNational identityNationalismState (computer science)DictatorshipPolitical economyIdentity (music)Political sciencePopulismPeriod (music)Economic historySociologyLawDemocracyHistoryAestheticsPhilosophyPolitics

Abstract

fetched live from OpenAlex

David Brandenberger argues that contemporary Russian identity was mainly a result of a “historical accident.” He maintains that this national identity was a product of the twentieth century rather than the nineteenth, which is more commonly cited, and that in terms of the state formulating a conception of what it meant to be Russian, the first decade of the Soviet period achieved little. However, by the late 1920s Soviet ideologists began to seek something more appealing than the mundane party slogans and eventually added non-proletarian, historical Russian heroes to the Soviet pantheon, particularly after the purges when the latter group was sorely depleted. This campaign was largely successful in inducing an understanding of national identity from a non-proletarian past as is evident today. He perceives this process as the formation of a Soviet populism, designed to mobilize society “on the mass level” and compares Stalin's USSR with Latin American dictatorships in this regard. Stalin, he argues, “was an authoritarian populist rather than a nationalist.” By 1953, Russians had a much better idea about their identity than in the period before 1937.

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)0.001
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: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0040.002
Open science0.0000.002
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 head0.028
GPT teacher head0.320
Teacher spread0.292 · 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
GenreOther

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

Citations2
Published2010
Admission routes1
Has abstractyes

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