L’étude du menu comme représentation de l’identité culinaire québécoise : le cas des menus au Château Frontenac
Bibliographic record
Abstract
Les arts de la table et la bonne bouffe font partie intégrante de la culture québécoise et lui donnent en grande partie son charme et son cachet. Avec plus de 400 ans d’histoire, la ville de Québec représente un lieu propice et riche non seulement pour l’étude de l’identité culinaire québécoise et franco-canadienne, mais aussi quant à l’évolution de la cuisine canadienne en général. Notre objet d’étude, le menu, se situe au croisement de l’historiographie, des food studies , des études canadiennes et de la traductologie. Nous y faisons valoir, à partir d’un corpus unique (les menus archivés du Château Frontenac), que le menu est un lieu discursif riche en pistes d’analyse et relativement inexploré. D’ailleurs, notre étude se propose d’analyser les normes langagières, sociales et culinaires présentes dans ces menus, et ainsi de dégager quelques tendances relatives aux identités culinaires québécoise, canadienne et franco-canadienne.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".