Geographies of indebtedness : the spatial nature and lived experiences of household debt in Metro Vancouver
Bibliographic record
Abstract
Since the 1990s, Canadian household debt levels have grown at an increasingly rapid rate, hitting records levels in late 2014. Mainstream representations paint the looming household debt crisis as a product of rampant overconsumption, underpinned by a societal lack of ‘financial literacy’. To what extent does the empirical evidence reflect such discourses? Few critical studies examine household debt in Canada, and still fewer consider the sub-national scale. According to existing scholarship, processes of financialization, securitization and neoliberalization influence household debt internationally and nationally. This thesis investigates the geography of high household debt levels at the local scale for Metro Vancouver. It examines the causes and consequences of heavy indebtedness in the everyday lived experiences of individual debtors, and the services and supports that they need to face these challenges. At the sub-CMA level, the spatial distribution of debt stress (debt-to-income and debt-to-wealth ratios) is assessed at the FSA level via a quantitative mapping analysis for Metro Vancouver – Canada’s most indebted city. Despite the generalized high debt stress across the CMA, a distinctly uneven spatial distribution of the costs, stresses (indebtedness) and benefits (gains in wealth) of rising mortgage and consumer debt levels emerges, with disproportionate stress in Vancouver’s outer suburbs. Through in-depth qualitative interviews with highly indebted Vancouverites, this thesis unpacks the everyday effects that increasing debt-loads have on residents of high-debt neighbourhoods. It draws on media discourse and debtor testimonies to consider the societal and survival pressures to engage in ever higher levels of borrowing experienced by financialized citizen subjects – funding consumption in a time of high income and wealth inequalities. Common causes of debt stress include unexpected life events such as divorce and job loss, easy accessibility of and marketing pressures to consume credit, and insufficient household resources as inflation surpasses wage growth. Financial literacy initiatives are called into question while, alongside issues of housing affordability, the study finds a critical need for lending regulations, non-predatory alternatives to small and short-term Payday loans, and reduced barriers to accessible and affordable mental health counseling for debtors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".