Bibliographic record
Abstract
On February 24, 2022, Russia escalated the ongoing Russo-Ukrainian war to a full-blown invasion of Ukraine. As a war tactic, Putin endorses gender-based violence by employing rape rhetoric to frame Ukraine as a powerless woman, and to demand the submissiveness that he believes is owed to him. To elucidate the socio-political forces behind gender-based violence as a war tactic, I reveal the relationship between traditional gender roles in Eastern Europe and how they establish the female body as the property of a nation. Through the examination of relevant literature, I draw a theoretical perspective that identifies the female body as nationalized, objectified as property, and inscribed as a site of violence. Applying this lens to the invasion of Ukraine, I identify the social and political forces that allow Russian soldiers to objectify the Ukrainian female body as a battle ground on which national wars are fought. Further, I discuss how gender-based violence, while apparent during peacetime, becomes amplified during conflict, and how this violence physically inscribes the Ukrainian female body as “Other.” To conclude, I discuss how the lived experiences of Ukrainian women become embodied through fear, yet silenced through the ongoing nature of this war, and I pose several questions that aim at creating space for women to share their painful experiences as an act of liberation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".