Your Fries are Less Fattening than Mine: How Food Sharing Biases Fattening Judgments Without Biasing Caloric Estimates
Bibliographic record
Abstract
Food sharing has become quite popular over the last decade, with companies offering food options specifically designed to be shared. As the popularity has grown, so too has concerns over the potential negative impact on consumer health. Despite companies’ explicit claims to the contrary, critics maintain that food sharing may be encouraging excessive caloric intake. The current article provides the first systematic exploration of why this may be happening. Three main and two supplementary studies suggest that food sharing reduces perceived ownership, which, in turn, leads people to mentally decouple calories from their consequence. Thus, sharing can reduce the perceived fattening potential of a consumption episode without biasing caloric estimates. This phenomenon persists even when explicit caloric information is provided, and it applies to both healthy and unhealthy foods. Importantly, we establish a relevant downstream consequence by illustrating that people tend to subsequently select calorie‐dense foods after underestimating the fattening potential of a shared consumption episode. A roadmap for future research and practical implications are discussed.
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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 | 0.010 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".