Bibliographic record
Abstract
Abstract This book offers a close examination of the ethical, political, and practical dimensions of donation-based online crowdfunding for basic needs including medical treatment, housing, food, and education. Crowdfunding uses online platforms and social networks to raise money from friends, family, and complete strangers for a variety of projects and needs. This practice has grown massively worldwide in recent years in terms of the numbers of crowdfunding campaigns and donors, money raised, visibility, and cultural influence. While the money raised through crowdfunding has helped millions of recipients, there is also reason for concern around how it may undermine campaigners’ privacy and dignity, mirror and exacerbate social inequities, mask and deepen social injustice, defraud donors, and spread misinformation and hate. The author places this discussion of crowdfunding in the wider historical context of giving practices and shows that crowdfunding can repeat and exacerbate ethical and political problems with traditional giving practices while creating other, new problems. While crowdfunding is often held up as a more democratic and less mediated giving practice than giving through philanthropies, crowdfunding platforms have a substantial role in mediating the relationship between donor and recipient, including determining what information is required in crowdfunding campaigns, influencing how viral crowdfunding campaigns are created, and deciding what kind of campaigns will be hosted. The author concludes by presenting nine values that should guide donation-based crowdfunding. These values can help crowdfunding donors, campaigners, recipients, platforms, and policy makers preserve the good that can come from crowdfunding while addressing some of its many negative aspects.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".