Understanding public attitudes and perceptions towards homelessness: a requisite frontier to reduce homelessness
Bibliographic record
Abstract
Canadian urban communities are continually being challenged to respond to the growing homelessness problem. While considerable Canadian research has been conducted regarding causes and solutions, an empirical void exists when it comes to Canadian attitudes and perceptions towards homelessness. Based on the premise that public opinions can influence how we think about social problems and influence and predict our behavior towards them, this study examined the attitudes and perceptions of 111 University of the Fraser Valley undergraduate students’ towards homelessness. Participants were recruited from four undergraduate classes across three faculties of study, and they completed 24 survey questions.\nSurvey results highlighted misinformation and stigmas about homelessness amongst participants. Respondents had a high level of awareness of the causes of homelessness and expressed sympathy for this population. Less support was found for having housing initiatives in their neighborhood, and much less support when it came to paying more in taxes. Most respondents were not aware of what Housing First was, or the fiscal benefit of investing in reducing homelessness. Moving forward, there is a need to foster a sense of community ownership for reducing homelessness. Community-based research may be needed to identify what people’s attitudes and perceptions are towards homelessness, followed by more broad examination of why people have the attitudes they do, and the subsequent development of strategies to educate and garner support for strategies and policies to reduce homelessness.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".