A Gamified System for Influencing Healthy E-commerce Shopping Habits
Bibliographic record
Abstract
Obesity is a serious health problem that has been linked to the major cause of death worldwide, ischemic heart disease. There is a lot of research on influencing people to live healthier lives by being active and eating healthy foods. However, there is little research on influencing people to buy healthier foods at the point of sale especially online. Because people tend to cook and eat what they buy, making healthier choices when grocery shopping online could lead to healthier eating habits for consumers. To advance research in this area, we propose a framework that uses gamification elements to influence consumers to purchase healthier foods in e-commerce. In this position paper, we present our proposed framework and describe the implementation of some of the influence strategies and game design elements such as rewards, personalization, suggestion, self-monitoring and feedback. This paper contributes to the area of game design by describing possible guidelines that could lead to healthier food shopping habits for e-commerce consumers.
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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".