Modernising charity law : recent developments and future directions
Bibliographic record
Abstract
In recent years the pressure for charity law reform has swept across the common law jurisdictions with differing results. Modernising Charity Law examines how the UK jurisdictions have enacted significant statutory reforms after many years of debate, whilst the federations of Canada and Australia seem merely to have intentions of reform. New Zealand and Singapore have begun their own reform journeys. This highly insightful book brings together perspectives from academics, regulators and practitioners from across the common law jurisdictions. The expert contributors consider the array of reforms to charity law and assess their relative successes. Particular attention is given to the controversial issues of expanded heads of charity, public benefit, religion, competition with business, government participation and regulation. The book concludes by challenging the very notion of charity as a foundation for societies which, faced by an array of global threats and the rising tide of human rights, must now also embrace the expanding notions of social capital, social entrepreneurism and civil society. This original and highly topical work will be a valuable resource for academics, regulators and legal practitioners as well as advanced and postgraduate students in law and public policy. Specialists in charity law, comparative law, and law and public policy should also not be without this important book.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".