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Record W3014148880 · doi:10.1007/s11266-020-00219-4

Food Banking and Food Insecurity in High-Income Countries

2020· article· en· W3014148880 on OpenAlexfundno aff
Laurie Mook, Alex Murdock, Craig Gundersen

Bibliographic record

VenueVOLUNTAS International Journal of Voluntary and Nonprofit Organizations · 2020
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
FundersUniversidad de AntioquiaEuropean CommissionMcGill UniversityUniversità degli Studi di Napoli Federico IIUniversity of South CarolinaYale UniversityEconomic Research ServiceU.S. Department of Agriculture
KeywordsFood insecurityFood securityCivil societyGovernment (linguistics)Food sectorBusinessPrivate sectorEconomic growthEconomicsPolitical scienceAgricultureGeographyPolitics

Abstract

fetched live from OpenAlex

Abstract Food banks are a particular type of voluntary sector organization that bridges the government sector, private sector, and civil society. This special issue of Voluntas adds to the stream of research on the role of food banks in addressing food insecurity in high-income countries. We begin by outlining the concept of food insecurity and a number of direct responses to alleviating food insecurity at the household and individual level by governments and the voluntary sector. We then look at the potential and limitations of food banks in addressing food insecurity in high-income countries, distinguishing between anti-hunger research and research framed as addressing community food security. Based on the set of seven papers included in this special issue, we call for further research that bridges both these approaches.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0000.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.045
GPT teacher head0.352
Teacher spread0.307 · 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 designObservational
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

Citations29
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueVOLUNTAS International Journal of Voluntary and Nonprofit OrganizationsSame topicFood Security and Health in Diverse PopulationsFrench-language works237,207