Virtual Reality–Based Food and Beverage Marketing: Potential Implications for Young People of Color, Knowledge Gaps, and Future Research Directions
Bibliographic record
Abstract
Unlabelled: Exposure to unhealthy food and beverage marketing is a major contributor to excessive weight gain among young people and it may disproportionately affect Black and Latinx communities. Appropriate and comprehensive regulations on food and beverage companies are essential, particularly as companies expand their reach and leverage the latest technologies to create marketing experiences using immersive virtual reality (VR). Although immersive VR technology is in its infancy, the potential effects of immersive VR food and beverage marketing on consumption, coupled with the history of racially targeted marketing by food and beverage corporations toward Black and Latinx communities, and the heightened burden of diet-related illnesses in Black and Latinx communities underscore a critical need to investigate immersive VR marketing targeting young people of color. This viewpoint will provide a brief description of VR food and beverage marketing as the newest food and beverage marketing frontier, highlight key concerns and knowledge gaps, and underscore future directions in research.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".