BALTA Project C16 - Strength, Size, Scope: A Survey of Social Enterprises in Alberta and British Columbia
Bibliographic record
Abstract
Assistance with the research was provided by students: Jeremy Arbuthnot, Jessica Baas, Tom Nelson and Samantha Sadler. \nOf the total of 4,500 employees, 60 percent or 2,700 employees were members of a designated target group such as persons with a mental or physical handicap or a member of a marginalized population. In addition, the social enterprises that responded to the survey engaged 6,780 full- and part-time volunteers and 27,870 people as members. These social enterprises were responsible for training 11,670 people and providing services to an additional 678,000 people. \nThe sale of goods and services in the market generated $78 million in revenue across the two provinces and an aggregate net profit of $7.9 million, in the 2009 financial year. Like other nonprofit organizations, social enterprises solicit non-market funds from a variety of funders, including foundations, government and individual donors. \nThe authors, Peter R Elson of Mount Royal University and Peter Hall of Simon Fraser University hope that this survey will establish a template for similar surveys of Social Enterprises across Canada and will provide a basis for tracking the progress of social enterprises in Alberta and British Columbia.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".