Bibliographic record
Abstract
Abstract Housing figures prominently during economic crises, a notable example being the Great Depression. Because housing is immobile, its market is very localized. In each city, the main agents are closely interconnected. Lenders depend on mortgaged homeowners and landlords to maintain payments; landlords rely on tenants; municipalities need all property owners to pay taxes. The Depression experiences of tenants, homeowners, and federal housing programs are well-appreciated; those of landlords and private lenders much less so. Considering the role of all agents, this case study of Hamilton, Ontario, focuses on owners and private lenders and asks who lost property, to whom, and how. Drawing on land registry and property tax records, city directories, and newspaper accounts, it documents the pattern and trajectory of defaults experienced by homeowners, landlords, and private lenders. Contemporaries and historians have used foreclosures as a measure of distress, but many borrowers defaulted voluntarily. The experience of Hamilton’s homeowners was similar to those in U.S. cities. Local landlords experienced higher rates of defaults than homeowners; private lenders foreclosed less often than lending institutions. Along with municipalities, both learned to be flexible in demanding payments. The high incidence of private mortgages, the stability of lending institutions, and the marginal role of the federal government were distinctively Canadian, but in general Hamilton’s experience is more broadly indicative.
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 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 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".