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Record W7132977932

Household Indebtedness and Socio-Spatial Polarization among Immigrant and Visible Minority Neighbourhoods in Canada's Global Cities

2014· dissertation· W7132977932 on OpenAlexaboutno aff
Dylan Simone

Bibliographic record

VenueTSpace · 2014
Typedissertation
Language
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationFinancializationDebtNeighbourhood (mathematics)Polarization (electrochemistry)Household debtHousing tenureDescriptive statistics
DOInot available

Abstract

fetched live from OpenAlex

Two key attributes of contemporary global capitalism are on the one hand, financialization and rising household indebtedness, and on the other, high levels of mobility and migration between nations, particularly into the `global' cities. Studies on household debt as it relates to race and immigrant status are scarce outside of the US. This thesis investigates levels and types of household indebtedness at the neighbourhood scale among immigrant communities and areas containing more racialized people, in the three largest Canadian cities - Toronto, Montreal, and Vancouver (TMV). In particular, it seeks to understand whether racialized and immigrant neighbourhoods experience higher and more onerous kinds of debt (such as unsecured forms of consumer debt) than other neighbourhoods, and the contours of any correlations between them. Descriptive statistics and regression models demonstrate that neighbourhoods housing immigrant groups, and certain visible minority groups, relate to higher levels of unsecured consumer debts in TMV.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.016
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.001
Scholarly communication0.0020.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.307
Teacher spread0.285 · 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
Published2014
Admission routes1
Has abstractyes

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