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Record W3111070805 · doi:10.5304/jafscd.2020.101.028

Fostering food equity in an immigrant neighborhood of New York City during COVID-19

2020· article· en· W3111070805 on OpenAlexfundno aff
Valerie Imbruce

Bibliographic record

VenueJournal of Agriculture Food Systems and Community Development · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
FundersBinghamton UniversityYork University
KeywordsChinatownImmigrationChinaGrassrootsCoronavirus disease 2019 (COVID-19)ReputationEquity (law)TourismGeographyPolitical scienceAdvertisingSociologyEconomic growthBusinessEconomicsPolitics

Abstract

fetched live from OpenAlex

Food equity includes the right to food that is cul­turally appropriate. Immigrant neighborhoods can be sites of contestation over who participates in the production, distribution, and consumption of food. Manhattan’s Chinatown is a good example of a neighborhood where food is central to its com­merce, cultural heritage, and reputation as a tourist destination. The coronavirus’ origin in China caused imme­diate material impact on Chinese restaurants and food purveyors in New York City as well as in other cities with major populations of Chinese people. Chinatown suffered disproportionate closures of its grocery stores, restaurants, and produce vendors due to COVID-19 as compared to other neighbor­hoods in NYC. The grassroots response to this crisis is a reminder that people have the power to use food to assert the society that they desire, to shape a highly contested urban space, and to claim their right to the city.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.477

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0140.002
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.141
GPT teacher head0.283
Teacher spread0.143 · 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 designQualitative
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

Citations4
Published2020
Admission routes1
Has abstractyes

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