Nutrition labeling, numerosity effects, and vigilance among diet‐sensitive individuals
Bibliographic record
Abstract
Abstract Numerosity effects have been investigated in the psychology and marketing literatures. While the effects are documented in outcomes including money and temperature judgments, the potential application and effects of numerosity for nutrition labeling remain unexplored. In this work, we propose that vigilance offers one circumstance when individuals might succumb to numerosity effects. Within the context of nutrition labeling, we propose that the increased vigilance that people with diet‐sensitive illnesses have for specific nutrients on nutrition labels, counter‐intuitively, exacerbates the numerosity effect. We demonstrate that those with diabetes and those with hypertension, for example, are more vigilant for information on nutrition labels relevant to their condition, sugar, and salt, and this greater vigilance counterintuitively leads them to exhibit greater numerosity effects for those nutrients, influencing their food perceptions. As an illustration, we find that a person with hypertension would consider a food product with, say, 3 g of sodium to have less sodium content and be more healthful than one with 3000 mg, although the quantities are equivalent. Our research highlights to policymakers that a “one‐size‐fits‐all” solution for nutrition labeling is not appropriate.
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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 | 0.002 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".