Bibliographic record
Abstract
Charitable giving in Canada has never been higher. Between 1992 and 2008, contributions to charitable organizations more than doubled, from $4 billion to more than $9 billion. Donations to charitable foundations grew at an even more remarkable rate: more than 250 per cent, over the same period. But those striking numbers mask more puzzling, some might say more worrying, trends. While donations overall have grown, not all charity types have shared equally in the gains. Religious charities and health-related charities have seen the lowest amount of growth. Meanwhile, the country’s larger charities and foundations have seen substantial increases in donations, but donation rates to smaller charities have been relatively flat. As for the donors, it’s almost entirely high-income Canadians who seem to be giving significantly more, while the rate of giving among middle-income and lower income Canadians has hardly grown at all. In fact, the share of people claiming tax credits for donations in each income group is actually in decline, meaning fewer of us seem to be giving to registered charities, while the richest Canadians are primarily responsible for the rise in donations. The reasons for these unusual trends are unclear — though there is some evidence that the more ethnically diverse our country has become, the less inclined we are to donate. And whether we should even be concerned about these uneven patterns — the wealthiest Canadians giving bigger cheques to the country’s biggest charities and foundations — is also an open question. But the uncertainty about what these trends mean, why they’re happening, and whether they’re even a problem, is not something we should take lightly. Changes may be coming soon to Canadian charity policy: Last year, a House of Commons committee began a sweeping study of charitable giving in Canada, and it is already evaluating dozens of suggestions for policy adjustments. But, while we can discern some patterns and trends in giving, the reality is that we actually still know very little about why Canadians give, and how we, as a society, want to change the way we give, if at all. And until we make the effort to learn considerably more, any policy changes aimed at altering the landscape for Canadian charities are at risk of being politically driven, rather than evidence-based, and they could very well end up creating more problems than they solve.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.034 | 0.012 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".