Trends in Sales and Industry Perspectives of Package Sizes of Carbonates and Confectionery Products
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".