Bibliographic record
Abstract
Canada is a federal parliamentary democracy, officially bilingual (English and French), and one of the most multicultural countries in the world. Indeed, more than one-fifth of Canada's population consists of first-generation immigrants, and a similar percentage classify themselves as visible minorities. A confederation of ten provinces and three territories, Canada has a current population of over 36 million people who live across an expansive geographic area that constitutes the second largest country in the world. In this multifaceted context, the social economy of Canada plays an important role in bridging the public and private sectors to form a strong social infrastructure (Quarter, Mook, & Armstrong, 2018). It constitutes a vast range of organizations guided by social objectives including nonprofit organizations such as charities, foundations, and social enterprises; and cooperatives both non-financial and financial. There are distinct traditions of the social economy in anglophone and francophone parts of Canada. There are also traditions specific to particular populations, such as the Black social economy (Hossein, 2013); and the Indigenous social economy (Restoule, Gruner, & Metatawabin, 2012; Sengupta, Vieta, & McMurtry, 2015; Wuttunee, 2010). In this review, we look at the anglophone research on the social economy, noting that there are also French-language research institutions and educational programs focusing on the social economy; however, a review of these is beyond our scope. After providing an overview of the concept of social economy in Canada, we summarize research on its scope and size in the Canadian context. Next we focus on voluntaristic behaviors of giving, volunteering (formal and informal), and participating. Our focus shifts to describing the infrastructure supporting research of the sector, including key academic and umbrella associations and networks, as well as formal and informal education programs. Finally, we describe key funders of social economy research including government and foundations.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.032 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".