BIA‐Obesity (Business Impact Assessment—Obesity and population‐level nutrition): A tool and process to assess food company policies and commitments related to obesity prevention and population nutrition at the national level
Bibliographic record
Abstract
Addressing obesity and improving the diets of populations requires a comprehensive societal response. The need for broad-based action has led to a focus on accountability of the key factors that influence food environments, including the food and beverage industry. This paper describes the Business Impact Assessment-Obesity and population-level nutrition (BIA-Obesity) tool and process for benchmarking food and beverage company policies and practices related to obesity and population-level nutrition at the national level. The methods for BIA-Obesity draw largely from relevant components of the Access to Nutrition Index (ATNI), with specific assessment criteria developed for food and nonalcoholic beverage manufacturers, supermarkets, and chain restaurants, based on international recommendations and evidence of best practices related to each sector. The process for implementing the BIA-Obesity tool involves independent civil society organisations selecting the most prominent food and beverage companies in each country, engaging with the companies to understand their policies and practices, and assessing each company's policies and practices across six domains. The domains include: "corporate strategy," "product formulation," "nutrition labelling," "product and brand promotion," "product accessibility," and "relationships with other organisations." Assessment of company policies is based on their level of transparency, comprehensiveness, and specificity, with reference to best practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".