Bibliographic record
Abstract
This paper concerns the role of genocide in collective memory and its function for national identity-building in post-Soviet Ukraine. Known as the Holodomor, Ukraine’s famine of 1932-33 has become an important part of the country’s national history. Upon gaining independence in 1991, the Ukrainian government set out to build and affirm a national identity distinct from Russia, grounded in Ukraine’s unique history and national myths. The claim to have undergone genocide as a nation in the Holodomor comprised part of this state-building project, though whether this claim is appropriate under international law has long been disputed. This paper examines the ways in which the Holodomor-as-genocide thesis was embedded in Ukrainian national identity, particularly under the administration of Viktor Yushchenko. Through the creation of new institutions, campaigns, and laws, the Ukrainian government sought to have the Holodomor recognized as genocide at the international and domestic levels, and to make its sacred commemoration a cornerstone of Ukrainian society. This narrative was deployed to unite the nation under a shared history of suffering that effaced politically inexpedient realities, such as cases of complicity in the Holodomor and the Shoah by Ukrainian elites. Narratives assigning blame to Ukrainian Jews and Russians alike delineated a narrow conception of the true Ukrainian nation to the exclusion of the alleged perpetrators. Further, it served to distance Ukraine from Russia by emphasizing the consequences of Soviet colonialism and the importance of Ukrainian collective memory as a matter of political sovereignty and cultural emancipation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".