Bibliographic record
Abstract
The landscape of charitable giving in Canada has been altered over the past decade by tax incentives favoring large gifts of capital. Next steps could include tax credits for donations of real estate assets and private company shares. This policy reform promises to increase charitable giving, broaden the donation base and make charities less vulnerable to market swings. These credits can be introduced in a manner consistent with existing laws that reduces the chance of tax system abuse. 1 Contribution limits increased from 20 percent to 75 percent of income per annum and 100 percent at death. 2 Statistics Canada does not break out giving data by type of asset or timing (i.e., gifts by will or life insurance). The absence of detailed tax data makes it difficult to state definitively the relative importance of capital tax incentives on giving. The sharp rise in giving is coincident with introduction of tax incentives. Wages and income grew by 87.5 percent from 1995 to 2007 versus 140 percent for donations. [CANSIM Table 380-0016]. In the two decades prior to 1996, giving tracked wages. Other factors affecting giving may include capital market growth, real estate values, lower tax rates, changing philanthropic attitudes, more fundraising, and increased concentration of wealth. Since 1996, successive federal governments in Canada have introduced more than 20 tax incentives to
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.540 | 0.249 |
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".