Review of Canadian literary fare by Nathalie Cooke, Shelley Boyd, with Alexia Moyer
Bibliographic record
Abstract
This review looks at Canadian Literary Fare by Nathalie Cooke and Shelley Boyd, with Alexia Moyer. The book gives an unconventional exploration of 'food voices' in Canadian literature. The authors examine the food narratives of celebrated Canadian writers, like Alice Munro, Eden Robinson, Fred Wah, M. NourbeSe Philip, Tomson Highway, Rabindranath Maharaj, and others. The book explores the interactions between literary characters and food, challenging preconceptions about Canadian cuisine. It highlights the voices of Indigenous and immigrant writers, emphasizing the role of food in decolonization and reshaping identities. The authors discuss iconic Canadian foods, the symbolism of food markets, and food as demonstrative of struggles with poverty. Canadian Literary Fare is a valuable resource for those interested in the interplay between food culture and identity. It provides a refreshing departure from traditional approaches, examining Canadian culture through alternative 'food voices'.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.010 | 0.021 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".