Cueing healthier alternatives for take-away: a field experiment on the effects of (disclosing) three nudges on food choices
Bibliographic record
Abstract
BACKGROUND: The current field experiment demonstrates the effectiveness of nudging to promote healthy food choices. METHODS: Three types of nudges were implemented at a take-away food vendor: 1) an accessibility nudge that placed fruits at the front counter; 2) a salience nudge that presented healthy bread rolls to be more visually attractive; and 3) a social proof nudge that conveyed yoghurt as a popular choice. We additionally assessed whether nudging effects would remain robust when a disclosure message was included. The field experiment was conducted over a seven-week period. The measured outcome was the sales of the targeted healthy food products. RESULTS: The accessibility nudge significantly increased the sales of the fresh fruits. The impact of the salience nudge was limited presumably due to existing preferences or habits that typically facilitate bread purchases. As the sales of the yoghurt shakes remained consistently low over the seven-week period the impact of the social proof nudge remained unexamined. Critically, disclosing the purpose of the nudges did not interfere with effects. CONCLUSIONS: Current findings suggest nudging as an effective strategy for healthy food promotion, and offer implications for topical debate regarding the ethics of nudges.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".