Prescriptive Remembrance of Ukrainian Canadian History in Marsha Forchuk Skrypuch’s Second World War Trilogies
Bibliographic record
Abstract
Abstract: The differences between the Soviet and Ukrainian versions of history have been at the center of diasporic historiography, yet only recently have they begun appearing in Anglophone children’s literature. Most North American children’s and YA books set in Second World War Eastern Europe tend to show Ukraine as just an extension of Russia and pay no attention to the distinctiveness of Ukrainian culture and history. However, this approach has been challenged by Marsha Forchuk Skrypuch, a bestselling and award-winning Ukrainian Canadian author of historical fiction, who for more than twenty-five years has been introducing previously ignored topics to Canadian children’s literature. This article argues that what Skrypuch does in her Ukrainian-themed texts can be described as “encouraged commemoration” or “prescriptive remembrance” of Ukrainian history and collective memory. Reading Skrypuch’s two Second World War trilogies, the author of this essay studies how the novels address the tensions between individual and collective forgetting and recollection of the traumatic experiences of Ukrainian children who have fallen victim to both the Nazis and the Soviets.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.014 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".