Examing a Federal Excise Tax on Sugar-Sweetened Beverages as a Measure of Health Promotion
Bibliographic record
Abstract
Rates of obesity and chronic disease have been on the rise in Canada, contributing to the burden on provincial and territorial healthcare systems at the expense of other public programs. Many of these illnesses – heart disease, type 2 diabetes, cancer – are nutrition related and are preventable. A major contributor to these illnesses is sugar and its overconsumption. Sugar – especially added-‐‑sugars – can be found in many packaged and processed foods with no additional nutritional value. Unfortunately, the negative health effects of unconsciously consuming sugar in abundance is not internalized by many Canadians. This effectively elevates the risk of disease, and subsequently an increased demand for healthcare services and creates a burden on the economy in general. Unfortunately, Canada’s existing health promotion and prevention initiatives do not seem to be achieving the goal of alleviating the unnecessary costs to the healthcare system in terms of affecting a change in behaviour of Canadians. As such, the choice of policy instrument should be reconsidered. This project will study the rationale for a tax on sugar-‐‑ sweetened beverages in Canada, its impact on the economy, and factors that must be considered when implementing such a tax.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".