The Role of Crowdfunding in Political Mobilization and Extremism in Canada
Bibliographic record
Abstract
Abstract In 2022, an anti-vaccine mandate protest in Canada received millions of dollars in support through online crowdfunding. This event catalyzed political crowdfunding in Canada by demonstrating its ability to disseminate ideological discourse and mobilize collective action. Given its newfound visibility and impact, this study examines the landscape of political crowdfunding in Canada. We examined 60 campaigns from the legal, current events and political categories on the crowdfunding platform GiveSendGo and classified campaigns into: COVID-19-related topics, alternative media and free speech, climate change skepticism, and other political campaigns. Thematic analysis of the interactive discourse between campaign hosts and donors revealed that many campaigns were motivated by defending individual rights and freedoms amidst perceived government overreach, which fuels a distrust towards authority, including the government and mainstream media. Our study suggests that political crowdfunding empowers individuals to symbolically reflect their political and ideological beliefs through financial donations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".