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Record W4390431405 · doi:10.1016/j.ajcnut.2023.12.019

Food Insecurity, Neighborhood Food Environment, and Health Disparities: State of the Science, Research Gaps and Opportunities

2023· article· en· W4390431405 on OpenAlexfundno aff
Angela Odoms‐Young, Alison Brown, Tanya Agurs‐Collins, Karen Glanz

Bibliographic record

VenueAmerican Journal of Clinical Nutrition · 2023
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
FundersOffice of Disease PreventionNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Nursing ResearchNational Cancer InstituteU.S. Department of AgricultureUniversity of ConnecticutUniversity of North Carolina at Chapel HillOffice of Naval ResearchFogarty International CenterSan Francisco State UniversityNational Heart, Lung, and Blood InstituteBaylor UniversityUniversity of California, San FranciscoNational Institutes of HealthNational Institute on Minority Health and Health DisparitiesMcGill UniversityHarvard T.H. Chan School of Public HealthUniversity of PittsburghOffice of Nutrition ResearchMemorial Sloan-Kettering Cancer CenterJohns Hopkins UniversityUniversity of WashingtonUniversity of MinnesotaOklahoma State UniversityCenters for Disease Control and PreventionUniversity of PennsylvaniaDrexel University
KeywordsHealth equityEnvironmental healthEthnic groupSocioeconomic statusPsychological interventionAgricultureFood insecurityPolitical scienceGerontologyEconomic growthMedicineFood securityHealth careGeographyPopulationNursing

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.005
Science and technology studies0.0020.004
Scholarly communication0.0070.007
Open science0.0020.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.583
GPT teacher head0.562
Teacher spread0.021 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations234
Published2023
Admission routes1
Has abstractno

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