Potential outcomes of work integration social enterprises for people who are homeless, at risk of homelessness, or transitioning out of homelessness
Bibliographic record
Abstract
Purpose Social enterprise has the potential to serve as a mechanism of social and economic opportunity for persons experiencing homelessness. This paper aims to identify potential outcomes of work integration social enterprises (WISEs) for people who are homeless, at risk of homelessness, or transitioning out of homelessness. Design/methodology/approach Searches of 14 databases were completed using keywords and subject headings pertaining to homelessness, social enterprise and employment, respectively. These searches were then combined to identify literature concerning WISEs with homeless populations. The initial search yielded 784 unique articles. Through screening, 29 articles were selected and independently coded to establish themes. Findings The analysis identified the potential for WISEs to contribute positively to the lives of the target population in the areas of connection to the community, employment skill building, mental health, personal agency and empowerment, relationship-building, structure and time use, financial stability and housing. There were less positive and mixed findings regarding substance use, crime/delinquency, physical health and transition to mainstream employment. Future research should further explore causal relationships between WISE approaches and strategies and their potential implications for persons emerging from homelessness. Originality/value Prior to this research, there have not been any recent publications that synthesize the existing body of literature to evaluate the potential outcomes of WISE participation for homeless populations. This paper lays the groundwork for future empirical studies.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".