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Record W2621243433 · doi:10.4000/books.pufr.4966

Representing War Trauma in Children’s Fiction: A Child in Prison Camp and Naomi’s Road

2007· book-chapter· en· W2621243433 on OpenAlexaboutno aff

Bibliographic record

VenuePresses universitaires François-Rabelais eBooks · 2007
Typebook-chapter
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSorrowTrial by ordealPrisonPoetryRelocationHistoryThe ImaginaryWorld War IIGender studiesPsychologyLiteratureSociologyCriminologyArtPsychoanalysisPolitical scienceLaw

Abstract

fetched live from OpenAlex

Representing War Trauma in Children’s Fiction: A Child in Prison Camp and Naomi’s RoadShizuye Takashima (1928-2005) and Joy Kogawa (b. 1935) were aged 13 and 7 respectively in 1942 when they were abruptly uprooted from their native Vancouver and confined in “relocation camps” in the interior of British Columbia, where they endured physical, emotional and economic hardships. Both girls were among the 21,700 Japanese Canadians who were forcibly removed from their Pacific Coast homes during the Second World War. Several decades after their ordeal, Takashima and Kogawa published A Child in Prison Camp (1971) and Naomi’s Road (1986) to make children acquainted with this painful episode of Canadian history. Although the issues addressed throughout these two highly poetic pieces of autobiographical fiction are complex—for they explore a war-related individual and collective trauma with historical precision—the language used in them is simple and direct. Both first-person narrators are young girls perceptively observing the world around them. In a time of great sorrow, they find comfort and delight in the spectacular scenery of the Rocky Mountains, evade reality through imaginary voyages to their former Vancouver homes or to exotic countries, and discover that musical enjoyment grants them the peace of mind they desperately need in a world shattered by violence.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.201
Teacher spread0.189 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2007
Admission routes1
Has abstractyes

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