Food and Women in an Italian-Canadian Novel: Tenor o f Love by Mary Di Michele
Bibliographic record
Abstract
Food is an important “identity marker” and plays a key role in the migration process: by consuming the food and maintaining the culinary habits of their country, migrants affirm their identity and culture. Moreover, food is often associated with memory and nostalgia for the country of origin: indeed, it is evident that the presence of food and its preparation in literature becomes a kind of mirror for society. In the case of Canada, this analytical perspective appears particularly interesting, because its cultural context is hybrid: half American, half European, and with a considerable number of immigrants from all over the world.In this “gastro-literary” journey I propose to take, I will try to show that nourishment is a solid and real principle in the construction of identity in Canada, through the works of Italian migrant writers. In this contribution, I will analyze the theme of food connected to pleasure in a novel by an Italian-Canadian writer, Mary Di Michele, entitled Tenor o f Love. I will mainly consider the passages in this novel in which culinary practices are used as metaphors for situations typical of Italian migrants to Canada. Thereafter, I will examine the close link between the search for identity and female authenticity present in the novel, and how Di Michele manages to deconstruct the cliches associated with Italian culture and tradition through the figurative value of nourishment, managing to restore, through writing, legitimacy to women.
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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 | 0.028 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".