Rooming House Residents: Challenging the Stereotypes
Bibliographic record
Abstract
This research bulletin reports on a survey of 295 residents in 171 licensed rooming houses in Toronto. One of the findings: the tenants are generally people on low incomes, but beyond that, it is hard to make generalizations. Roomers are a diverse group – probably more diverse than is generally assumed. The survey was carried out in order to learn about the health status of roomers and about factors that might contribute to the health of roomers. The study also provided information on other characteristics of roomers, some of which challenge common stereotypes of roomers as socially isolated, undereducated, unemployed individuals with multiple personal problems. This report summarizes the findings, which provide a demographic profile of Toronto’s rooming house residents. About a third of those interviewed were employed, nearly 15% had university degrees, and about 80% felt they had adequate social supports. Roomers include the working poor (not just those on social assistance), some well-educated individuals, and people who are by no means socially isolated. Other than the fact that most roomers live alone (since few rooming houses can accommodate couples or families), have low incomes, and are therefore often food insecure, it is not possible to make many valid generalizations about roomers’ lives.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".