Bibliographic record
Abstract
The gaping holes in the U.S. and Canadian social safety nets mean that many people live in a state of financial precarity that can instantly become untenable in the face of another big expense, such as a large medical bill or damaged property. Historically, people have turned to their communities, neighbors, families, and loved ones for help in these situations. Today, asking for money on the internet through crowdfunding is among the most popular ways of seeking and donating to charity, and for-profit enterprises have realized that tapping into this instinct for helping is extremely good business. GoFailMe reveals how these sites, most notably GoFundMe, enjoy massive revenue, without providing the help they promise. They fail most of their users while putting them through an emotional rollercoaster and using sneaky tactics to obscure that reality. With unprecedented access to interviews, surveys, and hundreds of thousands of crowdfunding cases across North America, Erik Schneiderhan and Martin Lukk take on pressing questions with critical insight: When do we turn to others for help? Who succeeds and who fails in the digital crowd? Whom do these sites benefit? Ultimately, the failure of GoFundMe and others is emblematic of the inability of the for-profit sector and Big Tech to engineer an end to social inequality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.523 | 0.283 |
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".