Vaccine related crowdfunding on a ‘Freedom Fundraising’ platform
Bibliographic record
Abstract
INTRODUCTION: Donation-based crowdfunding was heavily used during the COVID-19 pandemic. While most of these campaigns were uncontroversial, others spread misinformation or undermined public health. In response, mainstream crowdfunding platforms like GoFundMe restricted what campaigns they would host. This led some campaigns to shift to lesser-known and less restrictive crowdfunding platforms. While research on health-related misinformation on mainstream crowdfunding platforms is increasing, less is known about crowdfunding on less restrictive platforms like GiveSendGo. The aim of this study is to review vaccine-related crowdfunding campaigns on the GiveSendGo platform to better understand: 1) how vaccines are portrayed on GiveSendGo; and 2) how successful these campaigns have been at attracting financial support. METHODS: We searched the GiveSendGo crowdfunding platform for campaigns including "vaccine" or "vaccination". This process yielded 907 unique results which were then scraped for their campaign text and fundraising data. The authors reviewed these campaigns for fundraisers whose aims related to vaccines for humans and assigned campaigns as being for 1) Accessing vaccines; 2) creating Spaces for the unvaccinated; 3) helping Unvaccinated Individuals); 4) Advocacy about vaccines; 5) supporting Anti-Mandate actions; and 6) responding to Vaccine Injuries. FINDINGS: We identified 765 crowdfunding campaigns that raised $6,814,817 and requested $838,578,249. Anti-Mandate campaigns were most common, followed by Unvaccinated Individuals, Vaccine Injuries, Advocacy, Access, and Spaces. Only Access campaigns took a positive or neutral view toward vaccines. Themes of freedom and religion cut across campaign types with campaigns critical of vaccines invoking bodily autonomy and religious freedom as justifying their fundraisers. DISCUSSION: Very few of these fundraisers met their goals. With the exception of Access campaigns, they frequently contained highly polarizing language advocating against public health mandates, misinformation about vaccine safety, and language from bioethics and reproductive choice advocates. Restrictions on vaccine-related campaigns on the GoFundMe platform likely drove campaign creation on GiveSendGo.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".