Community Development Through Social Enterprise; A Case Study of a Vertical Farm Social Enterprise in Midland, Ontario for Women with a Lived Experience of Violence
Bibliographic record
Abstract
Violence against women is prevalent across Canada. Governments and organizations, work to support women who have survived violence, but are these efforts effective? Are they addressing the root causes of violence? Often programs mandated to support women who have survived violence tend to focus on addressing immediate needs through emergency shelters, and supportive counselling. Despite the importance of such programming, they are reactive instead of preventative. Using a case study of a social enterprise (Operation Grow) in Midland Ontario that was designed to reduce poverty, food scarcity, and isolation for women who have survived sexual and/or intimate partner violence. This research takes an in-depth look at the unique needs of women who have experienced intimate partner violence and/or sexual violence, then uses these findings to articulate their unique needs, and examine how social enterprises can be designed to meet these needs. The research identified six key design elements critical for social enterprises to best support women with a lived experience of violence. These critical components include: a holistic design which supports each asset area of a woman’s life, an intersectional feminist lens and gender-based analysis, an active valuation of women’s unpaid labour, flexible programming, supports to access material resources, space for women to have and use their voices. Social enterprises must also be designed to challenge the current economic and social order and their systems that produce and uphold oppression. They ultimately must work to empower women, inclusive of their unique identities and experiences.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| 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".