Do Consumers Think Front‐of‐Package “High in” Warnings are Harsh or Reduce their Control? A Test of Food Industry Concerns
Bibliographic record
Abstract
OBJECTIVE: This study aimed to test the industry claim that "high in" front-of-package (FOP) labeling systems are perceived as harsh and reduce consumers' control over food choices. METHODS: Respondents aged 16 to 32 years completed a between-group experimental task in an online survey (n = 1,000). Participants viewed a beverage with one of four FOP labels (text-only, octagon, triangle, or health star rating) and rated the label on its "harshness" and whether it made them feel more or less "in control" of their healthy eating decisions. RESULTS: Across all label conditions, at least 88% of respondents indicated the symbols were "about right" or "not harsh enough." At least 93% felt the symbols made them feel "more in control" or "neither less nor more in control." Participants viewing the health star rating were more likely to rate the symbol as "not harsh enough" and less likely to state that the symbol made them feel "more in control." CONCLUSIONS: There was no evidence to support industry claims that consumers perceive "high in" FOP symbols as harsh or as restricting their control. Indeed, most participants reported that the symbols were about the right harshness, and that they increased their control, including "stop sign" FOP symbols similar to those implemented in Chile.
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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 | 0.005 | 0.024 |
| 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".