Fundraising, grantmaking and regulatory issues: regulating good in bad times
Bibliographic record
Abstract
The mobilisation and distribution of philanthropy in response to disasters occur with legal and regulatory frameworks associated with trust and charity law, and are promoted – or inhibited – by a government’s policies supporting philanthropy. In this chapter, the legal tangles that often arise in the collection and distribution of disaster funds are assessed. Key issues with legal implications include fraud, beneficiaries who do not meet the test of being ‘charitable’, the problems created when the amounts raised are too small or too large for the intended purposes, and communities divided over the distribution of funds. The potential conflicts between charity law and the expectations of donors and affected communities are illustrated through two sets of case studies: fatal bus crashes some 60 years apart in England and Canada; and Australian natural disaster appeals nearly 50 years apart. Philanthropy is not the only means of compensating those affected by disasters, however. It works alongside, albeit independently from, insurance, government payments and tort awards for negligence. The illustrative cases, coupled with the rise of online crowdfunding and social media, point to the need for reforms, including greater coordination between, or at least consideration of, the multiple modes of compensation.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".