Dine and Dash: An Exploratory Application of Three Criminological Theories
Bibliographic record
Abstract
In this exploratory study, we tested the relevance of social learning, rational choice, and social control theories as explanations of “dining and dashing,” an act that has substantial financial implications for the restaurant industry yet one that has received almost no empirical attention. Dine and dash is defined as people using a food and/or beverage service that is expected to be paid for and leaving the premises with no intention of returning to pay. Using a survey sample of 358 undergraduate and graduate students from a Canadian university, we found partial support for social learning and rational choice theories. Individuals who knew someone else who had dined and dashed were more likely to dine and dash themselves (social learning theory) (OR = 17.85, p < .001). When a person thought they would suffer consequences (e.g., paying a fine), they were less likely to dine and dash (rational choice theory) (OR = 0.76, p < .001). Those who considered the benefits of dining and dashing were more likely to dine and dash (OR = 1.24, p < .01). No variables drawn from social control theory were related to dining and dashing.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".