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Record W2894726157 · doi:10.1002/oby.22311

Do Consumers Think Front‐of‐Package “High in” Warnings are Harsh or Reduce their Control? A Test of Food Industry Concerns

2018· article· en· W2894726157 on OpenAlexafffund
Rachel B. Acton, David Hammond

Bibliographic record

VenueObesity · 2018
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsUniversity of Waterloo
FundersCanadian Institutes of Health ResearchUniversity of WaterlooPublic Health AgencyPublic Health Agency of CanadaHealth Research
KeywordsControl (management)Test (biology)Symbol (formal)Task (project management)PsychologySign (mathematics)Social psychologyAdvertisingFront (military)MedicineBusinessComputer scienceEconomicsEngineeringMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.035
GPT teacher head0.288
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2018
Admission routes2
Has abstractyes

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