Disrupting Philanthropy? A Reality Check for Digital Crowdfunding
Bibliographic record
Abstract
ABSTRACT Crowdfunding promised to revolutionize philanthropy by using digital technology to make charitable giving cheaper, easier, and more accessible. Has this been realized in practice? We highlight three crucial questions for charity professionals and academic researchers to consider regarding crowdfunding's “disruptive” capacity, and we answer them in light of nearly a decade of research on crowdfunding for health care and related personal costs. We argue that crowdfunding's benefits have been largely overstated. Instead of offering a radically novel approach, it puts a digital spin on an outdated charity model. While potentially empowering fundraising recipients, it can significantly undermine their autonomy in practice. And although crowdfunding is commonly used to support health and medical costs, it promotes values and practices that ultimately harm public health systems. Our synthesis highlights the considerable progress scholars have made in understanding this extremely popular, if flawed, approach to charity, and we call for more critical analyses of crowdfunding as it continues to evolve, alongside research into alternative approaches to charitable giving.
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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 | 0.028 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.023 | 0.031 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".