Characterizing the Consumer Food Environment of Dollar Stores and Exploring Differences by Neighborhood Racial Composition
Bibliographic record
Abstract
OBJECTIVE: To characterize the overall availability, price, and promotional placement of food and beverage products at dollar stores and explore differences in the food environment by neighborhood racial composition in Atlanta, Georgia. METHODS: A cross-sectional assessment of the food environment was conducted at 25 dollar stores. Measures included availability, affordability, and promotion of fresh produce, salty snacks, sweet snacks, sugar-sweetened beverages (SSBs), and water. Store neighborhoods were categorized as majority-Black (MB) or non-majority-Black (NMB) neighborhoods using American Community Survey data. Kruskal Wallis and chi-square tests (test of independence) were used to test for differences across neighborhood racial composition. RESULTS: Only 2 stores sold fresh produce, whereas all offered and most promoted sweet snacks, salty snacks, and SSBs. Compared with NMB neighborhoods, prices for SSBs and salty snacks were significantly lower in MB neighborhoods (P < 0.05). CONCLUSIONS AND IMPLICATIONS: The dollar store food environment lacks fresh produce and comprises largely unhealthy food options. Findings suggest dollar stores in MB neighborhoods may provide lower prices for unhealthy food and beverages.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".