Food Insecurity Among Black Households in the Mississippi Delta
Bibliographic record
Abstract
Over 38 million Americans experienced food insecurity in 2020 and a disproportionate number of those people, over 21 percent, were Black Americans (USDA, 2021). While Black people across the country experienced food insecurity at disproportionately high rates, the Deep South’s prevalence of food insecurity continues to outpace much of the rest of America with three of the top five food insecure states (Mississippi, Louisiana, and Arkansas) comprising the Mississippi Delta (Henchy & Jacobs, 2020). There is a paradox at play in the Mississippi Delta region regarding its role as one of the top agricultural producers in the country but simultaneously home to some of the food insecure communities as well. Food insecurity is associated with a number of poor health outcomes including, but not limited to, decreased cognitive performance in children, increased anxiety, and depression in non-senior adults, as well as higher rates of diabetes, hypertension, and general increased rates of poor health (Gundersen, 2015). Black households in the Mississippi Delta experience a series of social determinants that contribute to the high prevalence of food insecurity in the region including poverty, racial residential segregation, social isolation, and lack of access to nutritious foods. Food Insecurity and its complexity of confounding factors leave researchers with a significant task to find leverage points at which community leaders, policy makers and other actors in the socioecological framework might reduce food insecurity in places with high food insecurity like the Mississippi Delta. This report recommends addressing food insecurity in the Delta through improving the local structure of information flows by offering education programs to boost enrollment in social welfare programs underutilized in the region.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".