Red Herrings and Misguided Approaches: Taxation and Regulation of Business Activities by Charities in the United States and Canada
Bibliographic record
Abstract
Most countries place various limitations on the ability of charities to conduct business activities in areas unrelated to their charitable purpose. In this paper, I examine this narrow aspect of the legal framework for charities in the United States and Canada. First, I will briefly provide some background on charities' conduct of such activities. Next, I will broadly outline the current law in each country, highlighting key similarities and differences. Then, drawing on historical evidence, I will try to explain why the relevant law in each country developed as it did. Finally, I will evaluate each country's response, focusing not only on the results of each but also on whether the concerns that led to each were well-founded. Through doing so, I will attempt to draw conclusions as to whether there is a clear winner between the two statutory schemes, and if the experiences of each country can provide guidance for future attempts at reform.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.010 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".