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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".