Canadian Social Enterprises: Who Gets the Non-Earned Income?
Bibliographic record
Abstract
Abstract For social enterprises (SEs), non-earned income remains an attractive and important form of financing. Yet, many of these funds are donated without serious and collective deliberation about the overall impact of these transfers on the composition of the sector. Various authors suggest that the recent professionalization of the broader third sector and the use of accounting frameworks that favour short-term measurable results—a trend which SEs exemplify—are having an impact on who and what gets funded. We test this hypothesis by investigating whether the distribution of non-earned income to SEs located in three different Canadian provinces can be explained by donor preferences for the following: (i) culture and arts-related social goods; (ii) SEs that are located in wealthier neighbourhoods; and (iii) SEs that are ‘visible’ beyond their locality. The paper briefly discusses the generalizability of the results and concludes with policy recommendations that emphasize the limits of SEs in achieving a core goal of welfare provision.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".