Scaling Up: The Convergence of Social Economy and Sustainability
Bibliographic record
Abstract
When citizens take collaborative action to meet the needs of their community, they are participating in the social economy. Co-operatives, community-based social services, local non-profit organizations, and charitable foundations are all examples of social economies that emphasize mutual benefit rather than the accumulation of profit. While such groups often participate in market-based activities to achieve their goals, they also pose an alternative to the capitalist market economy. Contributors to Scaling Up investigated innovative social economies in British Columbia and Alberta and discovered that achieving a social good through collective, grassroots enterprise resulted in a sustainable way of satisfying human needs that was also, by extension, environmentally responsible. As these case studies illustrate, organizations that are capable of harnessing the power of a social economy generally demonstrate a commitment to three outcomes: greater social justice, financial self-sufficiency, and environmental sustainability. Within the matrix of these three allied principles lie new strategic directions for the politics of sustainability.Whether they were examining attainable and affordable housing initiatives, co-operative approaches to the provision of social services, local credit unions, farmersâ markets, or community-owned power companies, the contributors found social economies providing solutions based on reciprocity and an understanding of how parts function within the wholeâan understanding that is essential to sustainability. In these locally defined and controlled, democratically operated organizations we see possibilities for a more human economy that is capable of transforming the very social and technical systems that make our current way of life unsustainable.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.040 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| 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".