The Political Is Profitable: Political Controversy and Litigation Crowdfunding Outcomes
Bibliographic record
Abstract
ABSTRACT Little research has been conducted on litigation crowdfunding, including how links to politically controversial causes may influence fundraising success. This study collected 500 crowdfunding campaigns for legal expenses initiated between December 27, 2022, and June 1, 2023, and 50 litigation campaigns with the highest fundraising totals on the GiveSendGo crowdfunding platform. These campaigns were categorized into politically controversial and non‐politically controversial categories. 236 (47.2%) politically controversial fundraisers received median of $579 of $30,000 requested from 8 donations. Two hundred sixty‐four (52.8%) non‐politically controversial campaigns requested median of $10,000 and raised median of $0 from 0 donations. Forty‐six of 50 campaigns with the highest fundraising totals were for politically controversial issues. The relative success of politically controversial litigation campaigns suggests that they benefit from this connection, and campaigners may be motivated to stress politically controversial elements in their campaigns. Non‐politically controversial campaigns with litigation needs may find it relatively difficult to support their needs through crowdfunding. Related Articles Brogan, M. J., and J. Mendilow. 2012. “The Telescoping Effects of Public Campaign Funding: Evaluating the Impact of Clean Elections in Arizona, Maine, and New Jersey.” Politics & Policy 40, no. 3: 492–518. https://doi.org/10.1111/j.1747‐1346.2012.00353.x . Jäckle, S., and T. Metz. 2017. “Beauty Contest Revisited: The Effects of Perceived Attractiveness, Competence, and Likability on the Electoral Success of German MPs.” Politics & Policy 45, no. 4: 495–534. https://doi.org/10.1111/polp.12209 . Pompl, S., and S. Gherghina. 2019. “Messages and Familiar Faces: Crowdfunding in the 2017 U.K. Electoral Campaign.” Politics & Policy 47, no. 3: 436–463. https://doi.org/10.1111/polp.12301 .
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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 | 0.016 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".