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Record W3161265822 · doi:10.3390/foods10051071

Trends in Sales and Industry Perspectives of Package Sizes of Carbonates and Confectionery Products

2021· article· en· W3161265822 on OpenAlexaboutno aff
Chloe Jensen, Kirsten Fang, Amanda Grech, Anna Rangan

Bibliographic record

VenueFoods · 2021
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
Fundersnot available
KeywordsR packageFood industryBusinessMarketingPer capitaGovernment (linguistics)Package designAgricultural economicsFood scienceEconomicsEngineeringMathematicsChemistryStatistics

Abstract

fetched live from OpenAlex

Discretionary food package sizes are an important environmental cue that can affect the amount of food consumed. The aim of this study was to determine sales trends and reported food industry perspectives for changing food package sizes of carbonates and confectionery between 2005 and 2019. Changes in package sizes of carbonates and confectionery were investigated in Australia, the USA, Canada, and the UK. Sales data (units per capita and compound annual growth rate between 2005 and 2019) were extracted from the Euromonitor database. Qualitative data (market research reports) on industry perspectives on package size changes were extracted from industry and marketing databases. Carbonate sales data showed increased growth of smaller package sizes (<300 mL) and a decrease in sales of larger package sizes (≥2000 mL) in all four countries. In contrast, confectionery sales data showed no consistent trends across the selected countries. No growth was observed for smaller confectionery package sizes but an increase in growth of larger package sizes (50–99 g, >100 g), including share packages, was observed in Australia. Qualitative data (n = 92 articles) revealed key reasons identified by industry for changes in package size related to consumer health awareness, portion size control, convenience, market growth, and government or industry initiatives. Monitoring of discretionary food package sizes provides additional insights into consumers’ food environment.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.211

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 head0.022
GPT teacher head0.289
Teacher spread0.267 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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