Half‐Carts: Partitioned or Divided Grocery Carts Lead to Greater Fruit and Vegetable Purchases in Supermarkets
Bibliographic record
Abstract
Before food portions are determined at home, they are determined at the supermarket. Building on the notion of implied social norms, we propose that partitioning a shopping cart for targeted healthy foods (such as fruits and vegetables) may increase their sales. A concept test for on‐line shopping (Study 1) suggests that partitions may be effective because they suggest purchase norms. An in‐store study in a supermarket (Study 2) reinforces the potential power of partitioned carts by showing that most shoppers purchased fruits and vegetables in quantities that were in proportion to the size of their allocated partition within a shopping cart. Using divided shopping carts (such as half‐carts) could be useful to retailers who want to sell more high‐margin produce, but they could also be useful to consumers who, in order to shop healthier, can choose to divide their own shopping cart in half with their jacket, purse, or briefcase.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".