Spatial and temporal patterns in Canadian COVID-19 crowdfunding campaigns
Bibliographic record
Abstract
Online charitable crowdfunding has become an increasingly prevalent way for Canadians to deal with costs that they would otherwise not be able to shoulder on their own. With the onset of COVID-19 and related lockdown measures, there is evidence of a surge in crowdfunding use relating to the pandemic. This study gathered, classified, and analysed Canadian crowdfunding campaigns created in response to COVID-19 from GoFundMe.com, a popular crowdfunding platform. Spatio-temporal analysis of classified campaigns allowed for observation of emergent trends in the distribution of pandemic-related need incidence and financial support throughout the pandemic. Campaigns raising money on behalf of established charities were the most common in the sample, and accounted for the greatest portion of funding raised, while campaigns for businesses made up a small proportion. Dense metropolitan areas accounted for the vast majority of campaign locations, and total sample funding was disproportionately raised by campaigners in Ontario and British Columbia.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".