Food and translation on the table: exploring the relationships between food studies and translation studies in Canada
Bibliographic record
Abstract
While ‘the relationship between food, culture and translation may be unduly neglected,’ an observation made by the organisers of the First International Conference on Food and Culture in Translation, this topic has, in fact, already ignited significant innovative research in Canada. This article first addresses some of the challenges associated with research in Food Studies (FS) in a bilingual, bicultural (Anglo-Saxon and French) context. While FS has somewhat established itself as field within the larger Humanities in the English-speaking world, this is less the case in French-speaking countries. This presents a challenge for Canadian translators and FS scholars alike, as some terms associated with the field do not translate seamlessly. While the ‘problem’ of untranslatability is not necessarily novel within Translation Studies (TS), it is interesting to note that food is usually deemed a ‘universal’; here, food proves a sort of cultural litmus test, both conceptually and linguistically. Further, the article will examine some of the theoretical overlaps between FS and TS. Of particular interest here are the shared (re)conceptualisations of textuality, consumption of a cultural Other, representation and cultural mediation. These theoretical overlaps will be illustrated using examples drawn from culinary exchanges and FS research in Canada. Examples, such as the translation of Canadian menus, cookbooks and food policies will also be explored and analysed.
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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 | 0.026 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.035 |
| Science and technology studies | 0.046 | 0.029 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".