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Record W1937287119

A Profile of Income Assistance Recipients in Winnipeg’s Inner City

2011· article· en· W1937287119 on OpenAlexaboutno aff
Byron Sheldrick, Harold Dyck, Troy Myers, Claudette Michell

Bibliographic record

VenueWinnSpace (University of Winnipeg) · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
Fundersnot available
KeywordsInner cityGeographySocioeconomicsSociology
DOInot available

Abstract

fetched live from OpenAlex

The objective of this study is to examine the experiences of welfare recipients with the welfare bureaucracy in the city of Winnipeg. For many inner city residents some form of income assistance is a vital part of their overall income and necessary for basic subsistence (food and rent). Consequently, the decisions of welfare officials are tremendously significant for these individuals and the treatment they receive at the hands of those officials will help structure their attitudes about the state, their conception of their place in society as citizens and their own sense of self-worth and self-esteem. Through a series of structured interviews with welfare recipients this study attempts to provide a picture of the nature and experience of those inner city residents that make use of the welfare system.
\nIt documents who these people are, the types of problems they experience with the welfare bureaucracy, their understanding of the welfare system, and the need for improved advocacy programmes to better enable them to navigate the system. Finally, it provides a glimpse at what welfare recipients understand to be the barriers and obstacles they face in moving away from welfare and into paid employment. To many, the results of the interviews will not come as a surprise. However, it is important to document in these results in a systematic way in the hopes that they will have an influence on policy makers.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.034
GPT teacher head0.195
Teacher spread0.161 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

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