The bio-food industry’s corporate political activity during Health Canada’s revision of Canada’s food guide
Bibliographic record
Abstract
INTRODUCTION: We analyzed the bio-food industry's corporate political activity (CPA) during the revisions of Canada's food guide between 2016 and 2019. METHODS: We undertook a content analysis of the websites of 11 bio-food industry organizations and of the briefs that 10 of them submitted to the Canadian House of Commons Standing Committee on Health, as part of this Committee's review of the food guide. Data were classified according to an existing conceptual framework. RESULTS: We identified 366 examples of CPA used by the bio-food industry during and immediately after the development of the food guide. Most of the industry actors opposed the guide's recommendations. The most common CPA strategies were information management (n = 197), used to create and disseminate information in industry's favour, and discursive strategies (n = 108), used to defend food products and promote the industry's position regarding the food guide. Influencing public policy (n = 40), by gaining indirect access to policy makers (e.g. through lobbying) and becoming active in government decision-making, as well as coalition management (n = 21), by establishing relationships with opinion leaders and health organizations, were also common strategies. CONCLUSION: Bio-food industry actors used many different CPA strategies during the revisions of the food guide. It is important to continue to document the bio-food industry's CPA to understand whether and how this is shaping public policy development in Canada and elsewhere.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".