Canadian Social Enterprises: Who Gets the Non-Earned Income?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it