Social enterprise policy and practice: Opportunities and challenges in rural Ontario – peer learning
Bibliographic record
Abstract
We know a significant portion of the Ontario’s nonprofit sector income is generated through earned revenue: 36% of the core nonprofit sector (excluding hospitals and universities) . And yet there are still few supports to build the capacity of community organizations to explore SE intentionally so they can maximize their earned revenue potential, and sustain and grow their social, economic, environmental and cultural impact. This is particularly true in rural communities. The Rural Social Enterprise Constellation (RSEC) connects, supports, and grows social enterprise (SE) in rural Ontario to address acknowledged gaps and opportunities. It is a unique partnership initiated in 2012 among a diverse group of supporters and doers of rural social enterprise, including consultants, postsecondary institutions, provincial networks, and rural community economic development intermediary organizations. Since 2012 RSEC has been connecting work that’s happening on the ground with policy and strategy at regional and provincial levels and developing a more comprehensive look at the scale of social enterprise in Ontario, and what systems can make it stronger. It’s part of a broader movement to strengthen the social economy in Ontario. Two papers will be presented – one focusing on peer learning and outcomes from the RSEC capacity building project supported by the Ontario Trillium Foundation and the other focusing on development systems and policy supports for rural social enterprise resulting from an RSEC research initiative funded by the Ontario Ministry of Agriculture and Rural Affairs (OMAFRA) New Directions program.
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.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.036 | 0.025 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".