Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it