Pathways to Sustainable Employment: Bridging Community Justice and Social Enterprise Programs for Criminalized Women
Bibliographic record
Abstract
Within the context of the Canadian criminal justice system, women face significant challenges when striving to secure and retain employment due to structural stigma and punitive practices. These challenges form barriers to accessing employment programs or sustainable career paths. To explore these barriers, a qualitative research study including semi-structured interviews and focus groups incorporating an arts-based project was conducted with fourteen women who navigate such obstacles. The arts-based experience mapping exercise served as a foundational generative method to magnify the women’s experiences and validated the transformative contribution of arts-based methods to research processes. Through examination of the intersections established between community-based justice organizations and social entrepreneurship programs, the participants determined how bridging these systems together can support criminalized women to attain sustainable economic security. The constructivist grounded theory analysis reinforced an urgent need for the justice sector to shift from existing institutional-based employment program models to sustainable community-based configurations. The findings from this analysis informed the author’s creation of the Comoptigen Theory and Program Implementation Framework, which provides principles and program components for guiding the creation of a bridged employment program for criminalized women. This study advances the discourses about how restorative trauma-informed approaches and community-based justice development initiatives can contribute to strengthening the social economy in Ontario. Further outcomes of the research include concrete recommendations and practices for how community justice non-profit organizations can deliver employment programs integrating trauma-informed models of collective care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.016 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".