Pinpointing Patterns of Violence: A Comparative Genocide Studies Approach to Violence Escalation in the Ukrainian Holodomor
Bibliographic record
Abstract
This article utilizes the case study of the 1930s Ukrainian Holodomor, an artificially induced famine under Joseph Stalin, to advance comparative genocide studies debates regarding the nature, onset, and prevention of large-scale violence. Fieldwide debates question how to 1) distinguish genocide from other forms of large-scale violence and 2) trace genocides as unfolding processes, rather than crescendoing events. To circumvent unproductive definitional arguments, methodologies that track large-scale violence according to numerically-based thresholds have substituted for dynamics-based analyses. Able to address aspects of the genocide puzzle, these methodologies struggle to incorporate cross-cultural contextual variation or elicit ripe moments for specific, real-time interventions. Demonstrating how genocide’s precise, changing dynamics can be mapped over its duration, I present and apply a new mixed methods methodology, affirming that historical cases can inform modern prevention efforts. By coding 1932–1933 Ukraine-specific correspondences to/from Stalin, I pinpoint the precise moment when genocidal intent and victim selection overlaps.
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 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".