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Record W3157170808 · doi:10.21226/ewjus645

Raphaël Lemkin, Genocide, Colonialism, Famine, and Ukraine

2021· article· en· W3157170808 on OpenAlexvenueno aff
Douglas Irvin‐Erickson

Bibliographic record

VenueEast/West Journal of Ukrainian Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsGenocideFaminePolitical sciencePoliticsThe HolocaustContext (archaeology)SovereigntyUkrainianLawCriminologyHistorySociologyLinguistics

Abstract

fetched live from OpenAlex

Many words have been used to name and describe the Great Ukrainian Famine of 1932-33, including “famine” and “catastrophe,” “the Holodomor,” and now “genocide.” Was the famine genocide? Was the famine part of a genocide? Is the word genocide an exaggeration? Is naming the famine a genocide part of an attempt to dramatize events for political purposes today? Is the refusal to call the famine a genocide an act of genocide denial? This article argues that, though more than seven decades have passed and the Soviet Union has come and gone, questions about genocide in Ukraine remain intertwined in the discourses and narratives surrounding conflicts over Ukraine’s economic, political, social, and cultural position between the European Union and the Russian Federation. Given the implications of this word—“genocide”—within the context of current conflicts over Ukrainian history and identity and even sovereignty, it is important to reflect on how this concept has been used and applied. This paper analyzes conflict in Ukraine in the 1930s using Raphaël Lemkin's definition of genocide, as opposed to the legal definition established by the UN Genocide Convention, and discusses the conceptual strengths of Lemkin's definition of genocide in terms of understanding a wide-spectrum of oppressive, repressive, and violent processes of empire-building and colonization that occurred in Ukraine, and which culminated in the Holodomor.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.357
Teacher spread0.308 · 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 teacher head, 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

Citations2
Published2021
Admission routes1
Has abstractyes

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