Bibliographic record
Abstract
Canadian constraints on political activity by charitable organizations have been based on traditional common-law distinctions between charitable activity and partisan political advocacy. This article evaluates proposals to expand tax preferences for political activities by charities and non-profit organizations. It examines both the structure of the charitable/non-profit sector and the patterns of charitable giving, and the relation of both to the sector's broader activities and other major funding sources, including direct government expenditures. It notes the historical reasons for accommodating charitable giving within the tax system, and the progressive erosion and concentration of the donor population in recent years. It suggests that expanding tax preferences for political advocacy by charities and non-profits will increase pressures on the existing funding bases for many organizations and will potentially undermine public trust in the sector, as increased political competition for financial and policy support reduces distinctions between the public-interest and rent-seeking activities undertaken by members of the sector. Any changes to existing laws should be integrated with existing regimes for the funding of political parties and election campaigns.
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.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".