The Long‐Term Impact of Positive and Negative Information on Food Demand
Bibliographic record
Abstract
We examine whether the effects of information about product labels change over time. The analysis is based on 110 adult (nonstudent) participants who participated in a two‐stage economic experiment taking place three months apart. In the first stage of the experiment, willingness‐to‐pay (WTP) was elicited for items that were stated to contain bovine growth hormones, genetically modified ingredients, ingredients that have been exposed to antibiotics, and ingredients that were irradiated. Depending on the treatment, each first‐stage auction was supplemented with positive or negative information about each of the labels. The second‐stage experiments re‐elicited WTP for the same group of participants and the same items accompanied with the original labels, but this time without any secondary information. Our results suggest that the adverse impact of negative information does not persist over time; whereas in the case of positive information, changes in WTP from the initial to the follow‐up auctions are not statistically significant. This study enhances our understanding of how consumers retain information over a longer time period and suggests that previous studies that measure labeling impacts on WTP using isolated, single‐shot experiments may overstate the longer term effects of labels and negative information.
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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 | 0.022 |
| 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".