Experiences of being homeless or at risk of being homeless among Canadian youths.
Bibliographic record
Abstract
A qualitative study was undertaken with four groups--immigrants, youths, Aboriginal people, and landlords--in order to explore, compare, and contrast diversity issues among the homeless population and those at risk of homelessness in a larger Canadian city (Calgary, Alberta) with a smaller city (Lethbridge, Alberta), to better understand their and to needs make recommendations for improvement in service delivery and policy formation. This paper focuses on the findings from our sample of youths who shared information on a range of factors that contributed to their being homeless or at risk of being homeless. The youths in this study also shared their positive as well as negative experiences with educators, peers, family members, and social service providers. Canada's homeless include growing numbers of young people, families, women, and members of various ethnic communities, including Aboriginal people and immigrants. Today it is no longer possible to articulate a single silhouette of the homeless, but rather a diversity of profiles is needed. It was in the light of this reality that a study, "Diversity Among the Homeless and Those At Risk," was carried out. It was undertaken with four groups--immigrants, youths, Aboriginal people, and landlords.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".