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Record W2051733794 · doi:10.1353/hpu.0.0167

An Ethnographic Study of Meal Programs for Homeless and Under-Housed Individuals in Toronto

2009· article· en· W2051733794 on OpenAlexaffabout
Naomi Dachner, Stephen Gaetz, Blake Poland, Valerie Tarasuk

Bibliographic record

VenueJournal of Health Care for the Poor and Underserved · 2009
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPovertyFood insecurityAgency (philosophy)MandateVulnerability (computing)MealWork (physics)GerontologyEnvironmental healthSociologyPsychologyPolitical scienceMedicineFood securityGeographyEngineeringSocial science

Abstract

fetched live from OpenAlex

Over the past two decades, Canada has witnessed a proliferation of community-based initiatives providing charitable meals to homeless and under-housed individuals. The existing research has raised concerns about the ability of such initiatives to meet users' nutrient needs. As part of a study of Toronto meal programs, open-ended interviews with program coordinators and observations of 16 meal programs were conducted to provide insight into the nutritional vulnerability of program users. Analysis using ethnographic methods revealed that, although charitable meal programs began in response to concerns about unmet food needs, the planning and delivery of meals are disconnected from the dietary needs of program users. Food was often a secondary service, designed to fit within the existing operations, resources, and mandate of the host agency. This work adds to calls for a rethinking of current responses to problems of hunger and food insecurity among individuals living in poverty in Canada.

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)0.003
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 score0.147
Threshold uncertainty score0.295

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0140.004
Scholarly communication0.0020.001
Open science0.0010.003
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 head0.106
GPT teacher head0.460
Teacher spread0.354 · 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

Citations37
Published2009
Admission routes2
Has abstractyes

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