Benchmarking the Nutrition-Related Policies and Commitments of Major Food Companies in Australia, 2018
Bibliographic record
Abstract
The food industry has an important role to play in efforts to improve population diets. This study aimed to benchmark the comprehensiveness, specificity and transparency of nutrition-related policies and commitments of major food companies in Australia. In 2018, we applied the Business Impact Assessment on Obesity and Population Level Nutrition (BIA-Obesity) tool and process to quantitatively assess company policies across six domains. Thirty-four companies operating in Australia were assessed, including the largest packaged food and non-alcoholic beverage manufacturers (n = 19), supermarkets (n = 4) and quick-service restaurants (n = 11). Publicly available company information was collected, supplemented by information gathered through engagement with company representatives. Sixteen out of 34 companies (47%) engaged with data collection processes. Company scores ranged from 3/100 to 71/100 (median: 40.5/100), with substantial variation by sector, company and domain. This study demonstrated that, while some food companies had made commitments to address population nutrition and obesity-related issues, the overall response from the food industry fell short of global benchmarks of good practice. Future studies should assess both company policies and practices. In the absence of stronger industry action, government regulations, such as mandatory front-of-pack nutrition labelling and restrictions on unhealthy food marketing, are urgently needed.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".