Probabilistic rewards increase the use of reusable cups
Bibliographic record
Abstract
• Single-use disposable cups cause pollution and release microplastics. • Reusable cups are an eco-friendlier and healthier alternative. • Probabilistic rewards (5 % chance to win a $5 gift card) increase reusable cup use. • Probabilistic rewards (10 % chance to win a free coffee) have similar effects. • Probabilistic rewards are a promising intervention to promote reusables. Given the environmental threat posed by single-use disposable cups, increasing the use of reusable cups among consumers is vital. To achieve this goal, the current study examined how probabilistic rewards influenced the use of reusable cups at cafés on a university campus. In the pilot study, customers with reusable cups were offered a 5 % chance to win a $5 gift card. This probabilistic reward produced a significant increase in the use of reusable cups compared to baseline. In a separate field experiment, one café offered customers with reusable cups a 10 % chance to win a free coffee, while another café served as a control site without the reward. The probabilistic reward led to a significant increase in the use of reusable cups. Together, these findings provide initial support for the implementation of probabilistic rewards to increase the use of reusable cups, with empirical, theoretical, and practical implications for sustainable consumption.
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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 | 0.000 | 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 | 0.000 | 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".