"Struggling Right Along With You": Precarity and the Power of Medical Crowdfunding Campaign Narratives
Bibliographic record
Abstract
Medical fundraisers—which feature patients or caregivers seeking funds for medical care, procedures, or other needs—are ubiquitous on social media, and US-based GoFundMe.com is one of the most popular platforms. The rise of platforms like GoFundMe as forms of medical care and triage is notoriously intertwined with the failures of the U.S. healthcare system. Medical crowdfunding campaigns in the U.S. span diverse topics, invoke a wide range of moral discourses, and are affected deeply by race, gender, class, religion, and (dis)ability. Drawing on insights from a discourse analysis of ten “trending” campaigns hosted on GoFundMe in 2019, I argue that campaigns are participatory narratives (because organizers, beneficiaries, and donors can interact within the campaign space) that rely upon an individualizing discourse of deservingness to create reciprocal ties within biosocial communities of care. As politico-moral projects, medical crowdfunding campaigns are at once reflective of and responsive to normalized precarity. Crowdfunding narratives are spaces in which idealized neoliberal citizen-subjects are produced and valorized collaboratively through the discursive work of campaign organizers and donors, limiting (and enabling) our imaginaries of community and care.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.024 | 0.060 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".