Ambiguity at home : unauthorized geographies of housing in Vancouver
Bibliographic record
Abstract
At least 20 percent of the city of Vancouver's rental housing consists of unauthorized secondary suites -- apartments built without permits inside detached houses. Local authorities have largely come to tolerate the existence of such units, seeing them as a vital means of dealing with the city's long-standing housing affordability problems. But despite the significance of this now-common local phenomenon, there is a tendency in the media, government documents and the popular imagination to view it from a singular perspective that reduces it to a strictly law-and-markets issue. Given Vancouver's highly competitive housing market, this restricted approach to thinking about the proliferation and lenient regulation of secondary suites is in many ways justifiable, but it has also served to erase a host of other important aspects of this local phenomenon. Examining the issue through the lens of various sub-fields in Human Geography, I seek to complicate its hegemonic understandings, and suggest that thinking about secondary suites from multiple perspectives can help us grasp many of the crucial geographical problems associated with contemporary life. I argue that even if legal or market frameworks are afforded privilege, there is more to be said about this issue, for example on the role of this so-called informal housing market in the local and global economy. The widespread notion that an insufficient supply of affordable housing is the main motivation to own or live in an unauthorized secondary suite is questioned using empirical evidence from the Census. The regulatory order to which these housing units are subject is shown to be less an effect of market forces than the historical product of a series of legal landmarks stretching from the mid-nineteenth century to today. Market forces are also shown to be a problematic explanation that obscures the role of politics and social norms in the formal and informal regulation of these apartments. In addition, I examine the politics of tenant/homeowner-landlord relations associated with this unconventional housing arrangement. Finally, I argue that secondary suites are not only an object of analysis for planning and other experts, but also a forgotten site of lay-knowledge production.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".