Doing good better: public policy for disaster philanthropy
Bibliographic record
Abstract
Philanthropy has become an integral component of disaster assistance, complementing the roles of government. In financial terms, the combined contributions of individuals, foundations and corporations are impressive. Among donating households worldwide, 41 per cent give in response to natural disasters (Double the Donation, 2022). Crowdfunding has accelerated the speed and magnitude of giving, as demonstrated by the success of entertainer Celeste Barber in raising over $50 million in a few days from people in more than 75 countries for those affected by the Australian bushfires of 2019– 20 (McGregor-Lowndes, this volume). In the early days of COVID-19, Captain Tom Moore ‘inspired’ others in the UK (and beyond) by attempting to walk 100 laps of his garden before his impending 100th birthday, with the goal of raising L1,000 for the government National Health Service (NHS). He ultimately raised L32 million (Pidd, 2021), although the foundation created by these donations has come under investigation by the charity regulator (BBC, 2022). Over the second year of COVID-19, global donations from foundations, corporations and high net worth (HNW) individuals to address the global pandemic topped over $20 billion, eclipsing giving to all other previous crises (Candid and CDP, 2021). While philanthropy is not – and should not pretend to be – a substitute for government funding and action in times of disasters, it offers some distinctive advantages over governments, as well as benefits to donors and those affected by disasters. Funds can be raised rapidly on an international scale, often without negotiation of jurisdictional boundaries or political considerations. Disbursements can be directed to local organisations to facilitate community-specific and place-sensitive responses. Donations may have few restrictions on their use and can facilitate risk-taking in a way that governments cannot. For donors, giving enables expression of shared grief and loss (McLean and Johnes, 1999), and produces a ‘warm glow’ that has a positive spillover effect on other giving, lifting rather than detracting from donations to other causes (Brown et al, 2012; Rooney, 2017; Scharf et al, 2021). It can enhance social capital with community-driven action (Xiang et al, 2021), thus contributing to longer-term organisational and community resilience. Corporate support and engagement with local business has been shown to restore market functioning and help local economies bounce back following a disaster more quickly than can government intervention (Ballesteros et al, 2017).
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.029 | 0.048 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.058 | 0.044 |
| Insufficient payload (model declined to judge) | 0.066 | 0.010 |
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".