Social entrepreneurship and services for marginalized groups
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine the usefulness of the social entrepreneurship (SE) framework in highlighting effective models of service development and practice in mental health equity. Design/methodology/approach – Using a rigorous SE search process and a multiple case study design, core themes underlying the effectiveness of five services in Toronto, Canada for transgender, Aboriginal, immigrant, refugee, and homeless populations were determined. Findings – It was found that the SE construct is highly applicable in the context of services addressing mental health inequities. In the analysis five core themes emerged that characterized the development of these organizations: the personal investment of leaders within a social justice framework; a very active period of clarifying values and mission, engaging partners, and establishing structure; applying a highly innovative approach; maintaining focus, keeping current, and exceeding expectations; and acting more as a service working from within a community than a service for a community. Practical implications – These findings may have utility as a guide for individuals early in their trajectories of SE in the area of mental health equity and as a tool that can be used by decision maker “champions” to better identify and support SE endeavours. Originality/value – In a context characterized by increasing attention given to models of SE in health equity, this study is the first to directly examine applicability to mental health equity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".