Inhabiting Memory in Canadian Literature / Habiter la mémoire dans la littérature canadienne
Bibliographic record
Abstract
This book examines the cultural work of space and memory in Canada and Canadian literature, and encourages readers to investigate Canada within its regional, national, and global contexts. It features seven chapters in English and five in French, with a bilingual introduction. The contributors invite us to recognize local intersections that are so easily overlooked, yet are so important. They reveal the unities and fractures in national understanding, telling stories of otherness and marginality and of dislocation and un-belonging. Ce livre examine l’importance culturelle de l’espace et de la mémoire en contexte canadien et plus spécifiquement dans les littératures du pays, afin d’inviter des lectures neuves des questions régionales, nationales et globales. Il rassemble sept chapitres en anglais et cinq en français, en plus d’une introduction bilingue. Les contributions, favorisant des approches thématiques et théoriques variées, sont réunies par leur désir de mettre en lumière des croisements inédits entre la mémoire et l’espace en tant qu’ils définissent certains des problèmes les plus brûlants de notre époque au Canada. S’y révèle l’équilibre fort instable entre récits unitaires et fractures communautaires, entre altérité et marginalité, ou entre dislocation et désappartenance. Contributors / Collaborateurs: Albert Braz, Samantha Cook, Jennifer Delisle, Lise Gaboury-Diallo, Smaro Kamboureli, Janne Korkka, André Lamontagne, Margaret Mackey, Sherry Simon, Pamela Sing, Camille van der Marel, Erin Wunker
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.035 | 0.029 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".