Crowding-Out Charitable Contributions in Canada: New Knowledge from the North
Bibliographic record
Abstract
Abstract: Using data from charitable organizations in the US, authors have established that government grants to charities largely crowd out giving from other sources, but that this reduction is due mostly to reduced fundraising activities of the charity itself. We use much more detailed data from over 6000 charities in Canada, measured for up to 15 years, to provide valuable new insights into this phenomenon. In particular, dollars received from individuals is largely unchanged by government grants. Instead, the crowding out is attributable to two other sources of donations not differentiated in US data: giving from other charities and charitable foundations, and donations gained from special fundraising activities, like galas or sponsorships. Only the latter—which is about half of the measured crowding out—represents a potential loss of dollars to the charitable sector as a result of government grants.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.020 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".