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Record W2981631930 · doi:10.3389/fbuil.2019.00121

Environmental Justice and the Food Environment in Prince George’s County, Maryland: Assessment of Three Communities

2019· article· en· W2981631930 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFrontiers in Built Environment · 2019
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
FundersJohns Hopkins University
KeywordsEthnic groupEnvironmental justiceGeographyDescriptive statisticsEnvironmental healthPopulationSocioeconomicsDemographyHousehold incomeGerontologyMedicineSociologyPolitical science

Abstract

fetched live from OpenAlex

Lack of access to a health-promoting food environment can lead to poor health outcomes including obesity which is a problem for African-Americans in Prince George’s County, Maryland. Previous research examined the quality of the food environment at the regional level but did not consider local level indicators. In this study, we utilized an environmental justice framework to examine the local food environment in the County. We collected data from 127 food outlets, (convenience stores, grocery stores, and supermarkets), in three racially and socioeconomically diverse communities – Bladensburg (predominantly African American/ Black, with the lowest median household income); Greenbelt (similar percentage of non-white persons as Hyattsville, with the highest median household income); and Hyattsville (dominated by a Hispanic population). We examined the availability, quality, and accessibility of food within each community, using a modified version of the Johns Hopkins Center for a Livable Future (CLF) healthy food availability index (HFAI).We also used ArcMap 10.6 to examine the spatial distribution of stores in relation to sociodemographic factors and generate descriptive statistics to examine HFAI score differences across the communities, sociodemographic composition, and store types at the block group level. Mean HFAI scores were 7.76, 10.75, and 9.60 for Bladensburg, Greenbelt, and Hyattsville, respectively suggesting a relative disparity in access to diverse healthy and good quality food sources for these communities although these differences were not statistically significant (p=0.79). Statistically significant differences between the communities were found with respect to ethnic stores, stores that sold fresh vegetables (p=0.047), and stores that sold fresh fruits (p=0.012). Getis-Ord Gi Hot Spot Analysis revealed one statistically significant cold spot at 95% confidence, and two others at 90% confidence in Hyattsville, indicating a cluster of low-scoring stores. The results indicate a potential need for expanded food infrastructure in these communities to improve public health. We also identified the need for culturally appropriate foods and proposed ethnic stores as potential salutogens to improve the food environment in culturally diverse neighborhoods.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.057
GPT teacher head0.335
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it