Community Voices: Insights on Social and Human services from People with Lived Experiences of Homelessness
Bibliographic record
Abstract
Community Voices is a participatory action research study conducted in collaboration with people with lived experiences of homelessness in Calgary, Alberta to gather insights into service provision. Following convenience and snowball recruitment strategies, seven focus groups with members of the homeless community were conducted by trained facilitators. Participants and other community members were invited as co-researchers to analyze focus group transcripts, highlight key issues, develop themes and recommendations, and share key findings with stakeholders. Study findings suggest that people who are homeless experience oppression at the personal, cultural and structural levels which make it less likely for them to exit homelessness. Our findings suggests that a housing first approach coupled with intensive personalized case management embedded within a human rights framework has the capacity to reduce homelessness and overcome the barriers that prevent individuals from exiting homelessness. Such interventions, however, require substantial investment to increase the stock of affordable housing units, improve current shelter facilities, educate personnel in anti-oppressive practices and a political commitment to recognize housing as a human right.
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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".