Taxation on Ultra-processed Foods to Trim Obesity: Is it Plausible in Canada?
Bibliographic record
Abstract
As the prevalence of excess body weight has become normalized in Canadian society, this paper arguesfor implementation of a sugar-sweetened beverages (SSB) and high saturated fat (SF) food taxation inCanada. These harmful foods and beverages are associated with excess calorie intake, lower nutrientintake, and a rise in body mass index. As the waistlines of Canadians continue to grow, it is of utmostimportance for obesity and overweight to be externally managed by the government with taxation onunhealthy substances, and a simultaneous subsidy on healthier alternatives. Potentially pairing SSB/SFtaxation with a fruits and vegetables subsidy could be one of the most effective means of achieving alteredconsumption patterns. The purpose is to curb availability of the former, increase consumption of the latter,and reduce weight gain and the harms that come along with it (e.g. metabolic disease and type II diabetes).The paper’s analysis focuses on children, adolescents (12-17 years old), and lower socioeconomic statuspopulations, as these populations are at a higher risk for overweight and obesity and would be mostpositively affected by the proposed taxation and subsidy. Briefly outlining the options governments have inreducing the levels of SSB/SF, questions are posed for future research regarding the area of ultra-processedfood taxations. Finally, notable objections to SSB/SF taxation are considered and alternative methods aresuggested such as income-based subsidy programs, which address inequitable distributions of proposedtaxation on vulnerable groups like children, adolescents, and lower socioeconomic status groups. Keywords: Canada, fat tax, obesity, subsidy, taxation, ultra-processed foods
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".