Worthy? Crowdfunding the Canadian Health Care and Education Sectors
Bibliographic record
Abstract
Crowdfunding, the practice of asking for money from others using the Internet, is a major private means through which Canadians are funding their health care and education. Crowdfunding has proliferated in Canada during the 2010s and continues to grow, approaching the revenues of Canada's major traditional charities. Proponents describe it as an empowering practice from which anyone can benefit. If its gains are inequitably distributed, however, increasing reliance on this private funding mechanism, especially in core areas of welfare state provision, can further exacerbate inequalities of opportunity and income. This study asks why Canadians turn to health care and education crowdfunding and how equitably funds are raised using this novel method. Based on a mixed methods analysis of 319 campaigns conducted on two prominent crowdfunding platforms between 2012 and 2014, we find that crowdfunding users' needs frequently correspond to known gaps in the contemporary social safety net, including in the area of cancer care, and that campaigns for older and visible minority Canadians face a disadvantage. We argue that health care and education crowdfunding is a response to the shortcomings of Canadian welfare state provision, but one that reproduces offline inequalities with potentially perilous consequences for democratic life and individual suffering.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".