Collaborative philanthropy and doing practically relevant, critical research
Bibliographic record
Abstract
Introduction The purpose of this chapter is to draw on experience doing collaborative philanthropy research, specifically on giving circles or giving collaboratives (GCs), to argue for doing practically relevant and critical research despite the potential challenges, such as philosophical and political tensions. GCs are collaborative forms of philanthropy in which members pool donations and decide together where these are given. They also frequently include social, educational and engagement opportunities for members, connecting them to their communities and to one another (Eikenberry, 2009). One example of a US-based GC is Washington Womenade, which holds regular volunteer-organised potluck dinners where attendees donate $35 to a fund that provides financial assistance to individuals (primarily women) who need help paying for things like prescriptions, utility bills and rent. In 2002, a Real Simple magazine story (Korelitz, 2002) on Washington Womenade led to the creation of dozens of unaffiliated Womenade groups across the country. This article also inspired Marsha Wallace to start Dining for Women, which is now a national network of more than 400 chapters across the US in which women meet for dinner monthly and pool funds they would have spent eating out, to support internationally based grassroots programmes helping women around the world. Another example of a GC in the UK is BeyondMe. It started in 2011 in London, made up of small groups or teams of young professionals affiliated with a particular corporation (for example, Deloitte or PwC) who select a charity or social enterprise with which to partner for the year, providing funding and professional pro bono support. Members of the team give ￡15 per month, with total funding to the beneficiary organisation amounting to between ￡3,000 and ￡5,000, and volunteer support of around 150 hours. Beneficiary organisations supported in the past include those helping jobless young offenders, homeless youth, women who have experienced abuse and sexual exploitation, and street and other marginalised youth, helping them to build businesses. It is impossible to say how many GCs exist, because of their grassroots nature; however, by many indications they are growing in number around the world. Dean-Olmsted, Benor and Gerstein (2014) estimate that one in eight American donors have participated in a GC. An increasing number of GCs operate in Canada, Japan, South Africa, Australia, India, China, Japan, Romania, Bulgaria, the UK, Ireland and elsewhere.
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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 | 0.047 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.109 |
| Scholarly communication | 0.028 | 0.026 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".