Hungry for Truth and (Hi)story: Images of Food in Alias Grace by Margaret Atwood
Bibliographic record
Abstract
Alias Grace is a historiographic metafiction written by Margaret Atwood in 1996 about Grace Marks, imprisoned for double murder. The style of the novel is a convincing reconstruction of the Victorian historical novel because of authentic descriptions of 19th century Canadian households and domestic life and its regular meals. The food motif is a vivid undercurrent in Alias Grace just as it is in Atwood’s other novels. Images of food intensify the realistic portrait of Canada, however, the food operates on a deeper, symbolic level: images of food, eating, and hunger are often interwoven with the power and class injustice. The analysis shows that hunger is not only physical experience and a hard fact of prisoner’s life but it can be metaphorical, manifested as hunger for truth and story. The article argues that imprisoned Grace controls her hunger to usurp responsibility for her story. It also illustrates that women are constantly associated with food and edibles and thus it points to related issues of cannibalism and power struggles. Although the motifs of food, eating and cannibalism have been discussed by numerous critics including Sarah Sceats, Heidi Darroch, and Sharon Rose Wilson, this article extends their research by exploring Atwood’s strategies of writing and storytelling using food images. The article examines Atwood’s postmodern technique of cooking up the Alias Grace from many historical texts and using genre fiction ingredients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".