Understanding Food Insecurity in the USA and Canada: Potential Insights for Europe
Bibliographic record
Abstract
Food insecurity is a leading nutrition-related health care issue in the USA due to the magnitude of the problem (almost 50 million Americans are food insecure) and its association with a wide array of negative health and other outcomes. Alongside this interest in the USA, there has also been growing interest in Canada. In contrast, food insecurity has received less attention in Europe. Nevertheless, there is both direct and indirect evidence that food insecurity and its attendant consequences are present in Europe. Given the similarities between the USA, Canada, and Europe, previous research can offer numerous insights into the causes and consequences of food insecurity in Europe and possible directions to address these through measurement and public policies. I first cover the methods used to measure food insecurity in the USA and Canada. In both countries, a series of 18 questions in the Core Food Security Module are used to identify whether a household is food insecure. I then briefly cover the current extent of food insecurity in each country along with some discussion of the recent history of food insecurity. A central advantage to using the Core Food Security Module in Europe is that the measure has been proven useful in other high-income countries, and using a standardized measure would allow for cross-country comparisons. I next cover two large-scale food assistance programs from the USA, the Supplemental Nutrition Assistance Program (formerly known as the Food Stamp Program) and the National School Lunch Program. For each, I summarize how the program is structured, how eligibility is established, and how participation proceeds. Europe has generally used income-based assistance programs to improve the well-being of low-income households; I consider a couple of reasons for why food assistance programs may also be worth considering.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".