Self-Managed Transitional Housing and Protected Work Facilitates a New Life for People who Are Homeless: A South African Case Study
Bibliographic record
Abstract
Provision of housing is well established as an important first step in assisting people who are homeless, preferably with additional service support and protected work. Cost and the high population of people experiencing homelessness complicates this level of resource provision in low- and middle-income countries including South Africa. ‘Streetscapes’ established a pilot housing programme at the beginning of the coronavirus disease 2019 (COVID-19) lockdown for people experiencing homelessness. Residents also engaged in the income support programme and received supplementary services. The residents set the rules for the house, made key decisions and shared duties in the house. A partial harm reduction approach was applied, in which residents could not use substances inside the house. Evaluation tools included nine in-depth interviews with residents and staff, organizational documentation and a monthly satisfaction survey. The first six months were characterised by conflict resulting from the COVID-19 lockdowns, but once these restrictions were lifted some residents left and the remainder established order in the house. The changes in the house allowed for the development of personal responsibility and of caring responses for each other. This greater control provided a context for the residents to develop themselves, come to terms with negative habits and to collect resources for their lives off the streets. Using people who had previously experienced homelessness as supervisors facilitated care and development.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".