All-you-can-eat in reality: You eat what you choose
Bibliographic record
Abstract
All-you-can-eat buffet is a long-standing favourite for gourmets and restauranters. In behavioral economics, literature show mixed findings in how the price interacts with customers’ perceived food quality and their consumption. Some research found that lower payment led to higher food consumption and worse ratings, while others found that this phenomenon to be associated with higher payment. This consistency may be due to the difference of experimental design; namely, whether participants were aware that different prices are available to choose from. If individuals can choose between two sets of two different prices, individuals who choose the cheaper set may consider their meal more cost-effective and will not push themselves to consume more, only to make up for the price. This study proposal aims to use Korean BBQ buffet to understand how the presence of choice impacts consumers’ behaviour. We hypothesize that when a choice is given, individuals in both the standard and the premium set condition will show similar results in all three variables. Conversely, when individuals do not have a choice, those who are assigned with the standard set will consume more food and give more negative ratings than those with the premium set. If this hypothesis is confirmed, results will have strong implications for both food industries and government policy-makers. For business, this implies that choice architecture can nudge consumers to make decisions that both lower operation costs and maintain customer satisfaction. For public welfare, the presence of choice can encourage an autonomous attitude in consuming healthy food.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".