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Record W4402287922 · doi:10.55016/ojs/sppp.v10i1.43228

Social Policy Trends: Calgary Food Bank Clients and Social Assistance Caseloads

2017· article· en· W4402287922 on OpenAlexaffabout
Ronald D. Kneebone, Margarita Wilkins

Bibliographic record

VenueThe School of Public Policy Publications · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBusinessSocial policyPolitical science

Abstract

fetched live from OpenAlex

CALGARY FOOD BANK CLIENTS AND SOCIAL ASSISTANCE CASELOADS The social safety net consists of various government programs as well as services funded and provided through different private organizations. The social safety net includes health and employment insurance, the Canada and Quebec pension plans, old age security, workers compensation, and provincial social-assistance programs. It also consists of the contributions of family, friends, and a great number of charities and faith-based organizations that seek to serve needs not directly met by government programs. In Calgary, part of the non-government sector that caters to those in need is the Calgary Food Bank. The points in the following graph, identify the number of social assistance caseloads (classified as Expected to Work - ETW) in the Calgary region, and the number of clients of the Calgary Food Bank for each month, beginning in January 2012 and ending in May 2017. The influence of the December holiday season on the number of clients served by the Food Bank is evident. Each December, the Food Bank serves, on average, the needs of an additional 6,700 clients relative to other months.

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)0.006
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 score0.395
Threshold uncertainty score0.795

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.009
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0210.002

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 head0.086
GPT teacher head0.394
Teacher spread0.309 · 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
Published2017
Admission routes2
Has abstractyes

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