Food choice mimicry on a large university campus
Bibliographic record
Abstract
Social influence is a strong determinant of food consumption, which in turn influences the environment and health. Purchasing mimicry, a phenomenon where a person copies another person's purchases, has been identified as the key governing mechanism. Although consistent observations have been made on the role of purchasing mimicry in driving similarities in food consumption, much less is known about the precise prevalence, the affected subpopulations, and the food types most strongly associated with mimicry effects. Here, we study social influence on food choice through carefully designed causal analyses, leveraging the sequential nature of shop queues on a large university campus. In particular, we consider a large number of adjacent purchases where a focal user immediately follows another user ("partner") in the checkout queue and both make a purchase. Across food additions purchased during lunchtime together with a meal, we find that the focal user is significantly more likely to purchase the food item when the partner buys the item, vs. when the partner does not, increasing the purchasing probability by 14% in absolute terms, or by 83% in relative terms. The effect is observed across all food types, but largest for condiments. Furthermore, purchasing mimicry is present across age, gender, and status subpopulations, but strongest for students and the youngest. We elucidate the behavioral mechanism of purchasing mimicry, and derive direct implications for interventions improving dietary behaviors on campus, such as facilitating preordering to reduce detrimental interactions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".