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Record W2324856241 · doi:10.1139/apnm-2012-0386

Nutrition marketing on processed food packages in Canada: 2010 Food Label Information Program

2013· article· en· W2324856241 on OpenAlex
Alyssa Schermel, Teri E. Emrich, JoAnne Arcand, Christina L. Wong, Mary R. L’Abbé

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2013
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMarketingBusinessNutrition informationFood marketingFood packagingFood scienceFood industryFood processingAgricultural economicsEconomicsChemistry

Abstract

fetched live from OpenAlex

The current study describes the frequency of use of different forms of nutrition marketing in Canada and the nutrients and conditions that are the focus of nutrition marketing messages. Prepackaged foods with a Nutrition Facts table (N = 10,487) were collected between March 2010 and April 2011 from outlets of the 3 largest grocery chains in Canada and 1 major western Canadian grocery retailer. The nutrition marketing information collected included nutrient content claims, disease risk reduction claims, and front-of-pack nutrition rating systems (FOPS). We found that nutrition marketing was present on 48.1% of Canadian food packages, with nutrient content claims being the most common information (45.5%), followed by FOPS on 18.9% of packages. Disease risk reduction claims were made least frequently (1.7%). The marketing messages used most often related to total fat and trans fat (15.6% and 15.5% of nutrient content claims, respectively). Limiting total and trans fats is a current public health priority, as recommended by Health Canada and the World Health Organization. However, other nutrients that are also recommended to be limited, including saturated fats, sodium, and added sugars, were not nearly as prominent on food labels. Thus, greater emphasis should be placed by the food industry on these other important nutrients. Repeated data collection in the coming years will allow us to track longitudinal changes in nutrition marketing messages over time as food marketing, public health, and consumer priorities evolve.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.232
Teacher spread0.219 · 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