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Record W2073270731 · doi:10.4236/aasoci.2014.41006

From Food Desert to Food Mirage: Race, Social Class, and Food Shopping in a Gentrifying Neighborhood

2014· article· en· W2073270731 on OpenAlexaboutno aff
Daniel Monroe Sullivan

Bibliographic record

VenueAdvances in Applied Sociology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsSocioeconomic statusSustainabilityRace (biology)Social classGeographyEthnic groupBusinessSocioeconomicsEnvironmental healthSociologyEconomicsDemographyMedicine

Abstract

fetched live from OpenAlex

New supermarkets in previous “food deserts” can benefit residents by improving their access to healthful, affordable food. But in gentrifying neighborhoods characterized by the inflow of middle-class, white residents and the outflow of working class, minorities, who benefits from a new supermarket that emphasizes organic food and environmental sustainability? This paper contributes to the food access literature by examining the food shopping behavior of diverse residents by using survey data and probability sampling in the Alberta neighborhood in Portland, Oregon (USA). Regression results show that college-educated (62%) and white residents (60%) are much more likely to shop there weekly, regardless of age, gender, owner-renter status, distance from supermarket, or length of time living in the neighborhood. These findings indicate that supermarkets that promote healthy living and environmental sustainability need to be sensitive to the racial “symbolic boundaries” and socioeconomic barriers that may create “food mirages” by limiting food access to poor and minority residents.

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 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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations64
Published2014
Admission routes1
Has abstractyes

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