‘I had never seen so many lobbyists’: food industry political practices during the development of a new nutrition front-of-pack labelling system in Colombia
Bibliographic record
Abstract
OBJECTIVE: To identify and monitor food industry use of political practices during the adoption of nutrition warning labels (WL) in Colombia. DESIGN: Document analysis of publicly available information triangulated with interviews. SETTING: Colombia. PARTICIPANTS: Eighteen key informants from the government (n 2), academia (n 1), civil society (n 12), the media (n 2) and a former food industry employee (n 1). RESULTS: In Colombia, the food industry used experts and groups funded by large transnationals to promote its preferred front-of-pack nutrition labelling (FOPL) and discredit the proposed warning models. The industry criticised the proposed WL, discussing the negative impacts they would have on trade, the excessive costs required to implement them and the fact that consumers were responsible for making the right choices about what to eat. Food industry actors also interacted with the government and former members of large trade associations now in decision-making positions in the public sector. The Codex Alimentarius was also a platform through which the industry got access to decision-making and could influence the FOPL policy. CONCLUSIONS: In Colombia, the food industry used a broad range of political strategies that could have negatively influenced the FOPL policy process. Despite this influence, the mandatory use of WL was announced in February 2020. There is an urgent need to condemn such political practices as they still could prevent the implementation of other internationally recommended measures to improve population health in the country and abroad, nutrition WL being only of them.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".