How do we Regulate Activities within a Charity Law Framework Focussed on Purposes
Bibliographic record
Abstract
The law regarding when an entity is or is not a charity focuses on that entity’s purposes, not its activities. Yet, much of the current civil society debate centres on particular charity activities: election campaigning, the discriminatory provision of goods and services, or carrying on large-scale commercial activities. The issue arises in many jurisdictions around the world. This article focuses on examples drawn primarily from Australasia to argue that there are alternative sources of regulation of relevance to Australasian charity activities and that evaluation and reform efforts would be best spent in focussing on these alternatives. In particular, broadly-applicable regulatory rules that apply to all entities engaging in activities such as anti-discrimination legislation; as well as charity-specific rules that might be justified as guarding against the charity/government or charity/business boundaries, or as rectifying charity deficiencies such as difficulties in raising equity capital. While the discussion focuses on how Australia and New Zealand might better regulate charity activities, the article informs that discussion by considering regulatory approaches to particular issues in the United Kingdom, Canada and the United States.
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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 | 0.061 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.074 |
| Scholarly communication | 0.027 | 0.028 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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".