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Record W3108866933 · doi:10.19195/0301-7966.58.5

“You will bear witness for us”: Suppressed Memory and Counterhistory in Marsha Forchuk Skrypuch’s “Hope’s War” (2001)

2020· article· en· W3108866933 on OpenAlexaboutno aff
Mateusz Świetlicki

Bibliographic record

VenueAnglica Wratislaviensia · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMilitary, Security, and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsUkrainianWitnessTheme (computing)HomelandMainstreamImmigrationHistorySpanish Civil WarSociologyWorld War IICollective memoryEmpathyMedia studiesLiteraturePsychologyArtLawPolitical sciencePoliticsSocial psychologyPhilosophy

Abstract

fetched live from OpenAlex

Marsha Forchuk Skrypuch’s historical novels and picturebooks for young readers have gained significant commercial and critical recognition in North America. Interestingly, Ukraine, her grandfather’s homeland, has remained the central theme in her works ever since the publication of the picturebook Silver Threads in 1996. The author of this essay argues that by telling the suppressed, untold stories, hence bringing attention to the next-generation memory of the traumatic experiences of Ukrainian Canadians, Skrypuch puts them on the landscape of Canadian collective and cultural memory and challenges the false generalizations attributed to Ukrainians and Ukrainian Canadians in North America after the Second World War. After briefly outlining the history of Ukrainian immigration to Canada, and explaining the roots of the negative stereotypes attributed to Ukrainians, the author analyzes Hope’s War (2001), Skrypuch’s first Ukrainian-themed novel, and shows that by highlighting the unexpected similarities between the experiences of the protagonist’s grandfather, who during the Second World War was a member of the UPA, and the anxieties of contemporary teenagers, Skrypuch evokes empathy in mainstream and diasporic readers and enables the formation of next-generation memory.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.297
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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