Retelling Stories of Resilience as a Counterplot to Homelessness: A Narrative Approach in the Context of Intensive Team-Based Housing Support Services
Bibliographic record
Abstract
This paper describes the use of narrative practices in the context of a Housing First program operated by the Saskatoon Crisis Intervention Service to help people in the process of overcoming homelessness tell their stories in ways that make them stronger. Housing First is an evidence-based intervention that offers immediate provision of permanent housing and wrap-around supports to individuals with persistent mental illness and other complicating co-morbidities who are experiencing homelessness. The paper arises from my reflections on learning whilst on social work practicum. Through participating in the narrative practice, people overcoming homelessness richly described their knowledge, skills, and abilities in getting through difficult times. This was effective in helping people to reacquaint themselves with a sense of purpose in life, while the audiences gave greater authentication and acknowledgement to people’s hopes and dreams for the future. This revealed that I could work more effectively by supporting people’s own initiatives rather than attempting to “fix” problems. What stood out the most was how the presenting problems were so closely correlated to larger and often oppressive social discourses. The linking of lives through shared purposes contributes to a collective voice that can amplify social issues and reverberate outward on a larger scale in the pursuit of social justice.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.016 | 0.028 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| 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".