Bibliographic record
Abstract
What might be the meaning of home care for Bryan? Bryan's situation is by no means unique. As the health-care reform movement has gained momentum, and as the drive towards home-care management has accelerated, homelessness and poverty have become realities in the lives of many. An October 1997 headline in the Globe and Mail read, Shelters running out of space: Warning sounded as winter looms. That same year, it was estimated that about 5,350 people in Toronto slept in shelters each night, compared to about 3,970 the year before. And the newspaper article reported that it was not only single men who faced homelessness; shelters for women and children were also full (Matas & Philp, 1997). The crisis of homelessness reflects, among other social issues, a rise in urban poverty. Lee (2000), using data from the 1996 Census and Statistics Canada's Low Income Cut-offs to measure poverty, found that between 1990 and 1995, poor populations in metropolitan areas grew by 33.8%, far outstripping population growth (6.9%) for the same time period (p. xv). Moreover, certain population groups were more likely than others to be poor. The average poverty rate among all city
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.056 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".