Should Canadian health promoters support a food stamp-style program to address food insecurity?
Bibliographic record
Abstract
Food insecurity is an urgent public health problem in Canada, affecting 4 million Canadians in 2012, including 1.15 million children, and associated with significant health concerns. With little political will to address this significant policy issue, it has been suggested that perhaps it is time for Canada to try a food stamp-style program. Such a program could reduce rates of food insecurity and improve the nutritional health of low-income Canadians. In this article, we explore the history of the US food stamp program; the key impetus of which was to support farmers and agricultural interests, not to look after the needs of people living in poverty. Though the US program has moved away from its roots, its history has had a lasting legacy, cementing an understanding of the problem as one of lack of food, not lack of income. While the contemporary food stamp program, now called Supplemental Nutrition Assistance Program (SNAP), reduces rates of poverty and food insecurity, food insecurity rates in the USA are significantly higher than those in Canada, suggesting a food stamp-style program per se will not eliminate the problem of food insecurity. Moreover, a food stamp-style program is inherently paternalistic and would create harm by reducing the autonomy of participants and generating stigma, which in itself has adverse health effects. Consequently, it is ethically problematic for health promoters to advocate for such a program, even if it could improve diet quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".