"Treats from a Belgian Cook": Food, Gender, and Ethnicity in the Canadian Tobacco Grower , 1960s–1970s
Bibliographic record
Abstract
Abstract: In the 1960s and 1970s, tobacco became an important agricultural commodity in Canada due to global demand for tobacco products. In regions like Norfolk County, the heart of Ontario's "tobacco belt," many families growing tobacco were first-generation European immigrants who were drawn to its profitability. During harvest, it was commonplace for farm wives to provide homecooked meals to seasonal agricultural labourers, and farms known to serve good meals attracted reliable workers. To assist cooks with this mammoth chore, a monthly column in the Canadian Tobacco Grower magazine offered recipes and tips. Drawing on these columns, this article examines how harvest meals not only contributed to a farm's financial success but also allowed women to preserve their ethnic identities and maintain traditional foodways. In the ongoing scholarship around gender, immigration, and food in the post-war period, this study contributes an important rural and agricultural dimension. Abstract: Dans les années 1960 et 1970, le tabac est devenu un produit agricole important au Canada en raison de la demande mondiale pour les produits de cette industrie. Dans des régions comme le comté de Norfolk, au cœur de la « ceinture du tabac » de l'Ontario, de nombreuses familles cultivant le tabac étaient des immigrants européens de première génération attirés par la rentabilité de cette activité. Pendant la récolte, il était courant que les femmes des agriculteurs fournissent des repas faits maison aux travailleurs agricoles saisonniers, et les fermes réputées pour leurs bons repas attiraient des travailleurs fiables. Pour aider les cuisinières dans cette tâche colossale, une rubrique mensuelle du magazine Canadian Tobacco Grower proposait des recettes et des conseils. S'appuyant sur ces chroniques, cet article examine la manière dont les repas de récolte ont non seulement contribué à la réussite financière des exploitations agricoles, mais ont également permis aux femmes de préserver leur identité ethnique et de maintenir leurs traditions alimentaires. Cette étude apporte une dimension rurale et agricole importante aux recherches actuelles sur le genre, l'immigration et l'alimentation dans la période d'après-guerre.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".