The structural roots of food insecurity: How racism is a fundamental cause of food insecurity
Bibliographic record
Abstract
Abstract Rates of food insecurity skyrocketed during the COVID‐19 pandemic, doubling overall and tripling among households with children. Even before the pandemic, the rate of food insecurity in the United States was “unusually high” compared to other rich democracies, and rates have not improved substantially over the last 25 years. What explains the lack of progress in addressing food insecurity? This article maps out an overview of the experiences, causes, and consequences of food insecurity in the United States. We demonstrate that racism is a fundamental cause of food insecurity, both because racism contributes to racial disparities in income and wealth, and because racism is linked to food insecurity independent of poverty and socioeconomic status. For example, people of color are more likely to experience racial discrimination, which is associated with food insecurity, and to live in states where stricter regulations and harsher punishments are tied to social assistance programs, including food assistance programs. Because racism is a fundamental cause of food insecurity, eliminating it requires going beyond “just” eliminating poverty. Instead, the fundamental cause must be tackled directly: racism itself, which is built into the structure of American society and entrenched in its institutions.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".