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Record W2772000172 · doi:10.3138/gsi.11.1.02

Starvation and Violence amid the Soviet Politics of Silence, 1928–1929

2017· article· en· W2772000172 on OpenAlexvenueno aff
Olga Bertelsen

Bibliographic record

VenueGenocide Studies International · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsnot available
Fundersnot available
KeywordsStarvationPoliticsSilencePolitical scienceCriminologyPolitical economySociologyLawMedicineArtInternal medicine

Abstract

fetched live from OpenAlex

This study analyzes the Soviet politics of silence during Stalin's collectivization campaign in the context of peasant resistance, state violence, and the famine in 1928–1929, and illuminates the primary function of strategic silence—an information blockade which creates a space for violence and human suffering. Only in silence does the landscape of violence emerge and its spiral dynamics consume everyone, assailants and victims, proceeding swiftly to the eventual destruction of this landscape. In Ukraine, strategic silence and the relatively hermetic information blockade highlights the intentional nature of state violence: it produced a ghetto of exclusion that helped crush peasant resistance to collectivization and prevented Ukraine's potential secession from the Union. More profoundly, the politics of silence is analyzed as “cultural” violence and one of the most important building blocks in the foundation of genocide that routinely provokes and escalates direct violence, a phenomenon which culminates in massacres, repressions, and famines, as happened in the Ukrainian case.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.088

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 studies0.0080.014
Scholarly communication0.0040.001
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.057
GPT teacher head0.405
Teacher spread0.348 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

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