Representing War Trauma in Children’s Fiction: A Child in Prison Camp and Naomi’s Road
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.029 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".