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Record W2735677561

An Examination of Community-based Meal Programs for Homeless and Under-housed People in Five Canadian Cities

2013· dissertation· en· W2735677561 on OpenAlexaboutno aff
Tyler Pettes

Bibliographic record

VenueTSpace · 2013
Typedissertation
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMealGerontologyGeographySocioeconomicsEnvironmental healthSociologyMedicineFood scienceBiology
DOInot available

Abstract

fetched live from OpenAlex

The provision of free or low cost meals is an integral component of community services working to address problems of poverty and homelessness in Canada. However, there has been little systematic examination of how they function relative to clients’ nutrition needs. The objective of this research was to investigate the scope and nature of meal programs by examining an inventory of charitable food provisioning activities, created between 2010 and 2011, in five Canadian cities. Of 290 agencies offering meal programs, 548000 meals were served every month. However, service scheduling varied throughout the week, and the majority of agencies were reliant on volunteer labour, donated food supplies, and experienced difficulties managing the current demand for food assistance. Findings from this study highlight the strengths and limitations of the current food provisioning system and a need to improve the capacity of agencies to respond to populations experiencing food insecurity 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.285

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0060.002
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.076
GPT teacher head0.435
Teacher spread0.359 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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