Tacos, Sriracha et sauce soya : le marketing qui nous fait aimer ces aliments venus d’ailleurs
Bibliographic record
Abstract
Les aliments « ethniques », appartenant à un répertoire culinaire venu d’ailleurs, sont aujourd’hui une réalité bien présente sur les tablettes d’épicerie et dans les garde-manger canadiens. Par le biais de quelles tactiques marketing ces aliments ont-ils pris leur place dans nos paniers d’épicerie et nos habitudes alimentaires ? Dans cet article, nous présentons trois études de cas (les tacos, la sauce Sriracha et la sauce soya) et dégageons quelques leçons tirées des tactiques marketing qui ont rendu ces aliments populaires. Ces cas soulignent, entre autres, le rôle et l’importance de l’entrepreneuriat et des influenceurs qui ont façonné la trajectoire commerciale de ces aliments ainsi que la relation que les consommateurs ont développée avec ceux-ci.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".