Documenting Food Safety Claims and Their Influence on Product Prices
Bibliographic record
Abstract
In response to increasing customer attention to food attributes, agribusinesses are employing novel product differentiation strategies. As an example, we investigate the use of food safety claims on new packaged food products from the food manufacturers’ perspective. First, using two product innovation databases, we investigate claim use on labels in seven English-speaking countries over the period from 1980 to 2008. Then, based on manufacturer recommended selling prices and using U.S. data (from 2002 to 2008), we apply parametric and nonparametric hedonic methods to identify supply-side (agribusiness) valuations of chemical and microbiological claims in two food categories. We identify a significant 5 cent premium per ounce for a “preservative free” claim in spoonable yogurts. We do not find a statistically significant impact for “ E . coli free” messages on meat and poultry products but find a significant price premium (19.3 cents and 25.7 cents per ounce in the two models) for “antibiotic free” claims in this category.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".