When Historical Fiction Meets Present-Day War: Marsha Forchuk Skrypuch’s Kidnapped from Ukraine Series and the Russia–Ukraine War
Bibliographic record
Abstract
This essay analyzes narrative strategy and ideology in children’s historical fiction, focusing on how Marsha Forchuk Skrypuch, a Ukrainian Canadian author of Anglophone historical literature, portrays the Russia–Ukraine war in the first two volumes of her Kidnapped from Ukraine series. Writing for North American readers who may be unfamiliar with the region’s complex history, Skrypuch draws on conflicting Russian and Ukrainian cultural memories of the Second World War to illustrate their ideological resonance in the present-day conflict. Through historical fiction tropes, Under Attack and Standoff explore the roles of young people – both Ukrainian and Russian – while exposing the contradictions of Russian propaganda and the enduring impact of disinformation. Drawing on the theoretical work of John Stephens and Jason Stanley, this essay argues that Skrypuch’s novels position readers to adopt a pro-Ukrainian, anti-war perspective while complicating simplistic binaries of heroism and villainy. Although Skrypuch's Ukrainian characters appear largely immune to propaganda, her Russian characters struggle to see beyond state narratives; therefore, she offers a nuanced yet ideologically charged depiction of the conflict.
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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 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".