Standardisation versus cultural adaptation in food advertising: insights from a two-culture market
Bibliographic record
Abstract
This article unravels principles of standardisation and cultural adaptation from past literature and empirically tests the applicability of these guidelines to food advertising, in a context in which two cultures, both with strong cultural differences with regard to food, are geographically integrated and share a common industry and market structure, thereby controlling for market and industry structure confounding factors. The study was conducted in the Montreal (Canada) area, comparing food TV ads targeted to two cultural groups—the French and the English Canadians. A total of 123 standardised ads are compared to 182 culture-specific ads (92 French and 90 English). Elements of advertising that are found amenable to standardisation pertain to product information and appeals based on basic positive emotions. Conversely, social and symbolical appeals, as well as appeals based on social emotions, present cultural specificity. Cross-cultural differences are found in culture-specific advertisements in the higher-order benefits associated with food (i.e. health for the English and pleasure for the French). Both benefits reflect global consumer trends and they are as frequently found in standardised ads as they are in corresponding culture-specific advertisements.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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 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".