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Record W2911516802 · doi:10.1007/s11266-019-00092-w

The Relationship Between Food Banks and Food Insecurity: Insights from Canada

2019· article· en· W2911516802 on OpenAlexafffundabout
Valerie Tarasuk, Andrée-Anne Fafard St-Germain, Rachel Loopstra

Bibliographic record

VenueVOLUNTAS International Journal of Voluntary and Nonprofit Organizations · 2019
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of Toronto
FundersInstitute of Population and Public Health
KeywordsFood insecurityBusinessFood securityGeographyAgriculture

Abstract

fetched live from OpenAlex

Abstract Food banks have become the first line of response to problems of hunger and food insecurity in affluent nations. Although originating in the USA, food banks are now well established in Canada, Australia, and some Nordic countries, and they have rapidly expanded in the UK and other parts of Europe in the past two decades. Defined by the mobilization of food donations and volunteer labor within communities to provide food to those in need, food banks are undeniably a response to food insecurity, but their relevance to this problem is rarely assessed. We drew on data from the 2008 Canadian Household Panel Survey Pilot to assess the relationship between food bank use and household food insecurity over the prior 12 months and examine the interrelation between food-insecure households’ use of other resource augmentation strategies and their use of food banks. We found that most food-insecure households delayed bill payments and sought financial help from friends and family, but only 21.1% used food banks. Food bank users appeared to be more desperate: They had substantially lower incomes than food-insecure households who did not use food banks and were more likely to seek help from relatives and friends and other community agencies. Our findings challenge the current emphasis on food charity as a response to household food insecurity. Measures are needed to address the underlying causes of household food insecurity.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.342
Teacher spread0.289 · 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.

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

Citations99
Published2019
Admission routes3
Has abstractyes

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