Between House and Home: Renovations Labor and the Production of Residential Value
Bibliographic record
Abstract
In 2014, spending on home renovations across Canada outstripped spending on actual home purchases. In this article, I explore this rise in renovations spending through a case study of these dynamics in the Greater Toronto Area (GTA), a metropolitan region that has experienced extraordinary growth in both house prices and levels of household mortgage debt over the last decade. While cheap mortgage debt has often been considered a key factor facilitating housing exchange and speculation in recent decades, I highlight the significant role that informalized renovations labor has played in these housing market dynamics across the GTA. Combining secondary data on GTA housing sales and renovations activity with in-depth interviews with precarious renovations workers, I contend that renovating has been a key strategy to overcome the crisis of affordability produced by low-interest mortgage debt. Highlighting the central role of renovations labor in reproducing the home as a commodity with either new use or exchange values, I recast strategies of asset wealth-building and house buying in the GTA as ones highly reliant on de-skilled and informalized noncitizen renovations labor. Informed by intersectional feminist scholarship on paid but precarious labor in the home, I offer a partial perspective on the fundamental importance of precarious renovations labor to the political economy of private homeownership.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".