Social purpose enterprises : case studies for social change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it