A conceptual limbo of genocide: Russian rhetoric, mass atrocities in Ukraine, and the current definition’s limits
Bibliographic record
Abstract
The presence of multiple, semantically opposed usages of the term “genocide” not only poses a challenge for legally defining Russia’s atrocities in Ukraine, but also exemplifies the constraints of international law in dealing with mass civilian destruction in the twenty-first century. Indeed, despite widespread evidence of Russia’s genocidal behaviour, few scholars and lawyers believe it would be legally possible to prove Russia’s genocide in Ukraine. Nonetheless, given the powerful public image of genocide as the “crime of crimes,” political usage of the term by politicians, activists, and the general public has intensified since the beginning of Russia’s 2022 invasion with the hope of attracting global attention to (and ceasing) Russia’s atrocities. This paper provides some preliminary observations on how and why the concept of genocide has proven to be effective in fuelling civilian destruction rather than preventing it during the invasion. It first traces how Russia’s controversial, two-pronged rhetoric of genocide has evolved over the initial months of the invasion. It then examines Russia’s atrocities and the difficulties of classifying them as genocide.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.036 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".