Racialized People, Women, and Social Enterprises: Politicized Economic Solidarity in Toronto
Bibliographic record
Abstract
For social enterprise to matter to racialized people, it must be purposefully embedded in the community. This study examines three nonprofit organizations led by women engaged in community economic development work – Firgrove Learning and Innovation Community Centre, Warden Woods Community Centre, and Elspeth Heyworth Centre for Women – in Toronto, one of the largest cities in North America. This study explores the work of these anti-racist feminist leaders who lack the certainty of funding from federal sources, yet understand that the key to making ethical community economies is to advance politicized economic solidarity and not to legitimize the corporatization of the social economy. This research also draws on the ethical coordinates of J.K Gibson-Graham to provoke a radical shift in the accepted understanding of social innovation in the enterprising development sector.HIGHLIGHTS Mainstream definitions of social enterprise exclude businesses led by marginalized peoples.Three racialized women in Toronto lead social enterprises with ethics and politicized action.These enterprises benefit their communities and fight racism in the capitalist economy.The study makes visible racialized peoples’ social-enterprise economy.Social enterprises must promote politicized economic solidarity and anti-racist feminism.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.019 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".