Additional file 1 of Food and beverage advertising expenditures in Canada in 2016 and 2019 across media
Bibliographic record
Abstract
Additional file 1: Supplemental Table 1. The 57 select product categories licenced from Numerator included in the study. Supplemental Table 2. Numerator’s methodology used to estimate expenditures by media. Supplemental Table 3. Thresholds against which products containing free sugars, added sodium and added fat are assessed to determine whether they would be classified as “permitted/healthy” or “restricted/unhealthy” advertising according to Health Canada’s proposed nutrient profile model. Supplemental Table 4. Changes in net food advertising expenditures on television in Canada† between 2016 and 2019, overall and by food category. Supplemental Table 5. Changes in net food advertising expenditures classified as “unhealthy” on television in Canada† between 2016 and 2019, overall and by food category. Supplemental Table 6. Changes in net food advertising expenditures on the radio in Canada† between 2016 and 2019, overall and by food category. Supplemental Table 7. Changes in net food advertising expenditures classified as “unhealthy” on the radio in Canada† between 2016 and 2019, overall and by food category. Supplemental Table 8. Changes in net food advertising expenditures in print media in Canada† between 2016 and 2019, overall and by food category. Supplemental Table 9. Changes in net food advertising expenditures classified as “unhealthy” in print media in Canada† between 2016 and 2019, overall and by food category. Supplemental Table 10. Changes in net food advertising expenditures in out-of-home media in Canada† between 2016 and 2019, overall and by food category. Supplemental Table 11. Changes in net food advertising expenditures classified as “unhealthy” in out-of-home media in Canada† between 2016 and 2019, overall and by food category. Supplemental Table 12. Net food advertising expenditures in digital media in Canada† in 2019, overall and by food category.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.386 | 0.037 |
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".