Stakeholder interactions with the federal government related to Bill S-228 and marketing to kids in Canada: a quantitative descriptive study
Bibliographic record
Abstract
BACKGROUND: , which subsequently died on the parliamentary table. This study quantified the interactions (meetings, correspondence and lobbying) related to Bill S-228 and children's marketing by different stakeholders with the federal government. METHODS: Interactions between all stakeholders and government related to children's marketing and Bill S-228 (Sept. 1, 2016-Sept. 30, 2019) were analyzed. These included the "Meetings and correspondence on healthy eating" database, detailing interactions between stakeholders and Health Canada related to nutrition policies; and Canada's Registry of Lobbyists, reporting activities of paid lobbyists. We categorized the interactions by stakeholder type (industry, nonindustry and mixed), and analyzed the number and type of interactions with different government offices. RESULTS: We analyzed 139 meetings, 65 lobbying registrants, 215 lobbying registrations and 3418 communications related to children's marketing and Bill S-228. Most interactions were from industry stakeholders, including 84.2% of meetings (117/139), 81.5% of lobbying registrants (53/65), 83.3% of lobbying registrations (179/215) and 83.9% of communications (2866/3418). Most interactions (> 80%) in the highest-ranking government offices were by industry. INTERPRETATION: Industry stakeholders interacted with government more often, more broadly and with higher ranking offices than nonindustry stakeholders on subjects related to children's marketing and Bill S-228. Although further research is needed to analyze the nature of the discourse around children's marketing, it is apparent that industry viewpoints were more prominent than those of nonindustry stakeholders.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".