Bibliographic record
Abstract
Remnants of Nation is a ground-breaking book that introduces a new category of analysis, 'poverty narratives/ through which literature and popular culture are studied in the larger context of economic and literary disenfranchisement.Although issues of race, gender, and sexuality are now circulating in literary studies, and their 'constructedness' is being debated, the relations of class, poverty, and narrative have not been thoroughly examined until now.Here, poverty is treated not simply as a theme in literature but as a force that shapes the texts themselves.Rimstead adopts the notion of a common culture to include more ordinary voices in national culture, in this case the national culture of Canada.Short stories, novels, autobiographies, and oral histories by Canadian women, including canonized writers such as Gabrielle Roy, Margaret Laurence, and Alice Munro, are considered in addition to lesser-known writers and ordinary women.Drawing on theoretical work from a wide range of disciplines, this book is a deeply radical reflection on how literature, popular culture, and academic discourse construct knowledge about the poor in wealthy countries like Canada and how the poor, in turn, can inform the way we think about nation, community, and national culture itself.Given the scope of the study, Rimstead's work will appeal not only to literary scholars and Canadian social historians, but also to students and instructors of women's studies, cultural studies, and sociology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.719 | 0.416 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".