Bibliographic record
Abstract
A considerable momentum has developed around the perceived need for a national affordable housing strategy. The design of any such strategy should recognize who is in need, the size of the need, and where that need is greatest. This report presents facts on the affordability of housing for those at risk of the most serious form of housing crisis, namely, the threat of homelessness. The facts span the period 1990-2014 to better understand if housing affordability is a new issue or one of long-standing. The facts identify the affordability of housing in each of Canada’s nine largest urban centers because national averages have little relevance for describing housing markets that are decidedly local. The facts focus on the affordability of the lowest-cost housing available to the very poor and identify the affordability of housing for different family compositions and for different types of accommodations. These facts show that the affordability of housing for the very poor is not, and has not always been, uniformly bad in all cities and for all family compositions. In some cities and for some family compositions however, the affordability crisis has been very serious and prolonged and shows little sign of abating. Any housing strategy must recognize these facts and needs to target support to those most in need.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".