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Record W4411749923 · doi:10.55016/ojs/sppp.v18i1.80050

Food Bank Use Prior to Homelessness

2025· article· en· W4411749923 on OpenAlexaff
Ali Jadidzadeh, Ronald D. Kneebone

Bibliographic record

VenueThe School of Public Policy Publications · 2025
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFood insecurityBusinessFood securityGeographyAgriculture

Abstract

fetched live from OpenAlex

Food provided by a food bank is a close substitute for food purchased in a retail store. This characteristic of food bank services means that threats to one’s ability to maintain housing – increased rents, job loss, inadequate income supports, high prices generally – can be expected to result in growing reliance on food banks. This policy brief derives, presents, and evaluates preliminary results from an ongoing study of how individuals and families respond to shocks to their budgets that present challenges to their ability to maintain housing. We hypothesize that such a shock sets in motion an effort by a household to conserve income for the payment of rent and so a coincident increase in its use of food banks. By linking administrative datasets reporting food bank use and entry into homeless shelters by uniquely identified people, we show how reliance on food banks increases as individuals and families near the date when housing is lost. This research has the potential for identifying periods of intervention that may prevent homelessness.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.002

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 head0.215
GPT teacher head0.476
Teacher spread0.260 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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