Bibliographic record
Abstract
Since the 2010s, all levels of governments in Canada have gradually initiated social procurement as a policy tool to further their social values and political agendas. Social enterprises of various shapes and sizes across the country have served as partners in the execution of those agendas. Selling Social examines the experiences of these enterprises in social procurement and social purchasing. Selling Social presents the findings of a three-year Canadian research project detailing experiences of work integration social enterprises (WISEs) selling their goods and services to organizational purchasers, including governments, businesses, and non-profit organizations. Drawing on survey findings and interviews, the book explores a diverse group of social enterprises from across Canada, showcasing their successes and their challenges based on real-life examples to aid social enterprises that are considering this path. The book emphasizes the importance of including social and environmental considerations in procurement and purchasing decisions, particularly at larger scales and through public policy. In doing so, Selling Social extends the understanding of social enterprises beyond their social and economic outcomes and into the broader movement towards responsible procurement and purchasing.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.002 |
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; both teacher heads agree on what is shown here.
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".