Understanding Food Access in Flint: An Analysis of Racial and Socioeconomic Disparities
Bibliographic record
Abstract
The primary objective of this study was to describe the food landscape of Flint, Michigan, and the surrounding townships. We investigated the relationship between the location of food outlets and the racial composition of census tracts. We collected data from multiple sources; however, Data Axle, a repository of information on the U.S. and Canadian businesses, was our primary data source. Data were collected and verified between September 2020 and December 2021. The final fact-checking was completed in June 2022. We used ArcGIS 10.8.1 and SPSS Version 28 to map and analyze the data. We conducted negative binomial regression analyses to identify the difference in the likelihood of finding food retailers in census tracts where the percentage of Black residents was low and those where the percentage of Blacks was high. The article examines 1,137 food retailers in the study area: 407 were in Flint, and the remainder in the surrounding townships. Restaurants—especially fast food and take-out establishments—dominated the food environment. In addition, small groceries and convenience stores proliferated in the grocery store category. The racial composition of the census tracts mattered. Census tracts in which more than 40% of the residents are Black have a mean of 7.6 food outlets. In comparison, census tracts in which 40% or less of the residents are Black have a mean of 11.3 food outlets; the difference is significant. Census tracts with a high percentage of Blacks also had significantly fewer restaurants. The results of this study show Flint’s food landscape to be more complex and robust than described in earlier studies. It underscores the point that researchers should not rely solely on documenting the presence of supermarkets or traditional grocery stores when addressing food insecurity and food access. In the case of Flint, such food outlets comprise only 2.2% of the food landscape. Focusing exclusively on these food retailers misses several important types of food venues that residents rely on to secure food. This siloed approach also ignores the resilience and ingenuity of residents to respond to limited access to traditional food retailers.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.078 | 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".