Bibliographic record
Abstract
This article addresses the emergence of the Canadian Holocaust literature canon for young readers, closely examining the work of Carol Matas and Kathy Kacer to explore how the Holocaust can be narrated for children. Largely understudied despite their productivity and popularity, Matas and Kacer rely on the narrative strategy of blending invented or imagined characters with factually accurate situations and experiences. By using the tools that historical fiction offers, these two prolific Canadian authors demonstrate the possibilities of multifaceted, educational, and engaging texts about the Holocaust for young people while preserving the “open hearts” of the characters at the centre of their stories.Cet article traite de l’émergence de la littérature canadienne sur l’Holocauste pour les jeunes lecteurs, en examinant de près le travail de Carol Matas et de Kathy Kacer pour explorer comment l’Holocauste peut être raconté aux enfants. En dépit de leur productivité et de leur popularité, Matas et Kacer n’ont pas fait l’objet d’études approfondies. Elles s’appuient sur une stratégie narrative qui consiste à mêler des personnages inventés ou imaginés à des situations et des expériences factuelles exactes. En utilisant les outils qu’offre la fiction historique, ces deux auteures canadiennes prolifiques démontrent les possibilités de textes à facettes multiples, éducatifs et engageants sur l’Holocauste pour les jeunes,
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.039 | 0.022 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".