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
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, farmers’ 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 whole—an 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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.017 | 0.010 |
| Research integrity | 0.000 | 0.000 |
| 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 it