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Record W2333547768 · doi:10.1177/02601079x10002200404

Barriers to Improved Capability for Low-income Canadians

2010· article· en· W2333547768 on OpenAlexaffabout
Jerry Buckland, Antonia Fikkert, Rick Eagan

Bibliographic record

VenueJournal of Interdisciplinary Economics · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsQueen's University
Fundersnot available
KeywordsSnowball samplingPersonal incomeBusinessLow incomeLimitingPublic relationsMarketingDemographic economicsEconomic growthPolitical scienceEconomicsMedicineEngineering

Abstract

fetched live from OpenAlex

This article examines barriers to improved well-being for low-income Canadians. It uses the capability approach to explore how personal, institutional and banking factors interact to create obstacles to improved capability. It does this by relying on financial life histories from 15 low-income people living in inner-cities in Toronto, Vancouver and Winnipeg. The financial life histories are a qualitative method in which respondents were recruited using a snowball sampling method, and asked to share personal stories about their life and financial goals. Respondents are, by Canadian standards, acutely poor and several faced multiple personal barriers including mental illness and substance abuse. The results indicate that participants experienced many more periods of declining, as compared with improving, capability. Respondents identified a series of personal (e.g., illness and addiction), structural (e.g., poorly funded education and low-levels of social assistance support), and banking (e.g., high banking fees and limited appropriate services) obstacles to their improved capability. Most respondents noted that they faced several obstacles at once that created powerful unfreedoms to improved capability. Weak banking services in the neighbourhoods was an important factor in limiting the capability of the respondents.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.378
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.006
GPT teacher head0.235
Teacher spread0.230 · 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.

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

Citations1
Published2010
Admission routes2
Has abstractyes

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