Cultural Schemas for Racial Identity in Canadian Television Advertising
Bibliographic record
Abstract
Quelles significations sont rattachées à la race dans la publicité? Nous analysons un échantillon de publicités télévisées canadiennes aux heures de grande écoute dans le but d'identifier des schémas culturels associés à ce que cela signifie d’être blanc, noir ou d’Asie de l’Est ou du Sud‐Est. Notre analyse empirique porte sur la publicité alimentaire et pour restaurants. Par l'entremise d'une analyse quantitative de contenu portant sur les liens entre la race et les sous‐types d'aliments, nous démontrons des différences systématiques dans les types d'aliments associés aux différents groupes étudiés. Au moyen d'une analyse qualitative, nous éclairons ces modèles quantitatifs et présentons six schémas culturels de l'identité raciale. Ces schémas appréhendent à la fois la diversité et le privilège associés aux représentations des personnes de race blanche, et des contrastes frappants par rapport au statut et à l’émotivité parmi les représentations plus limitées des deux autres groupes. What meanings are attached to race in advertising? We analyze a sample of prime‐time Canadian television advertising to identify cultural schemas for what it means to be White, Black, and East/Southeast Asian. Our empirical focus is on food and dining advertising. Through quantitative content analysis of associations between race and food subtypes, we show that there are systematic differences in the types of foods that groups are associated with. Through a qualitative content analysis of the commercials, we illuminate these quantitative patterns and discuss six cultural schemas for racial identity. The schemas allow for both diversity and privilege in the representation of Whites, and poignant contrasts regarding status and emotionality in the narrow representations of the other two groups.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 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".