Social purpose enterprises : case studies for social change
Bibliographic record
Abstract
Preface 1. Social Purpose Enterprises: A Conceptual Framework JACK QUARTER, SHERIDA RYAN, ANDREA CHAN Section A: Marginalized by Stigma 2. Common Ground Co-operative: Supporting Employment Options FRANCES OWEN, ANNE READHEAD, COURTNEY BISHOP, JENNIFER HOPE, JEANNETTE CAMPBELL 3. When the Business is People: The Impact of A-Way Express Courier KUNLE AKINGBOLA 4. Miziwe Biik Case Study: Microloans in the Urban Aboriginal Community MARY FOSTER, IDA BERGER, KENN ROSS, KRISTINE NEGLIA 5. Groupe Convex: Measuring its Impact USHNISH SENGUPTA, CAROLINE ARCAND, ANN ARMSTRONG Section B: Women on the Social Margins 6. Inspirations Studio at Sistering: A Systems Analysis AGNES MEINHARD, ANNIE LOK, PAULINE O'CONNOR 7. Micro-Entrepreneurs in Economic Turbulence: The Alterna Savings Micro-Finance Program EDWARD T. JACKSON, SUSAN HENRY, CHINYERE AMADI 8. Canadian Immigrants and their Access to Services: A Case Study of a Social Purpose Enterprise MARLENE WALK, ITAY GREENSPAN, HONEY CROSSLEY, FEMIDA HANDY 9. Wellbeing of Childcare Workers at the Learning Enrichment Foundation, a Toronto Community Economic Development Organization ANDREA CHAN, ROBYN HOOGENDAM, PETER FRAMPTON, ANDREW HOLETON, EMILY POHL WEARY, SHERIDA RYAN, JACK QUARTER Section C: Urban Poor and Immigrants 10. Doing Markets Differently: FoodShare Toronto's Good Food Markets MICHAEL CLASSENS, J.J. MCMURTRY, JENNIFER SUMNER 11. Stakeholders' Stories of Impact: The Case of Furniture Bank ANDREA CHAN, LAURIE MOOK, SUSANNA KISLENKO 12. Northwood Translation Bureau JENNIFER HANN, DANIEL SCHUGURENSKY Section D: Youth 13. Market-based Solutions for At-Risk Youth: River Restaurant RAYMOND DART 14. Social Purpose Enterprises: A Modified Social Welfare Framework JACK QUARTER, SHERIDA RYAN, ANDREA CHAN Contributors
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".