Talking about Homelessness and School: Recommendations from Canadian Young People Who Have Experienced Homelessness
Bibliographic record
Abstract
The primary research question driving this paper is the following: “What are the schooling experiences of young people who are at risk of or experiencing homelessness?” Through interviews with 28 young people in two cities in Ontario, Canada, the authors identified several common experiences, including the following: lack of available information that could help them cope with their housing difficulties; prejudice and bullying from other students, sometimes stemming from their housing problems but also due to factors such as racialization, gender identity, poverty, and substance use; and individual support from some teachers and support staff, although this was dependent on being in school. They proposed changes to help young people still in school, including the inclusion of non-judgmental information and guidance on dealing with poverty and homelessness in school curricula, educating school staff about the ‘symptoms’ of homelessness to help them identify students at risk, and creating more safe and supportive school environments overall.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.035 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".