Bibliographic record
Abstract
Housing is a major productive factor in the Canadian economy, generating community investment and economic development, jobs and consumer spending. Residential housing spending contributed over $70 billion to the Canadian economy in 2002 (Canada Mortgage and Housing Corporation, 2004). Building one new home creates nearly three jobs per year, while sales of existing housing account for over $7 billion in consumer spending and contribute to the creation of around 100,000 jobs annually (Adair, 2003). Access to adequate and affordable housing is also essential to the health and well-being of individuals. When people are well housed, their family and community life is more stable, enabling greater opportunities for good health, educational performance, job security and community safety. Internationally, adequate shelter is recognized by Canada as a basic human right. Although a majority of Canadians are well housed, a conservative estimate suggests that more than 100,000 people in Canada have no homes at all. A further 1.7 million Canadians, or nearly 16 percent of the population, are in core housing need – that is, they are unable to afford shelter that meets accepted adequacy, suitability, and affordability norms (Canada Mortgage and Housing Corporation, 2004). The incidence of core housing need is lowest in Alberta and highest
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.774 | 0.446 |
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".