Engagement and Crowding-Out Effects of Leaderboard Gamification on Medical Crowdfunding
Bibliographic record
Abstract
Prior research offers a minimal amount of direct evidence on the effect of gamification features such as top-donor leaderboards on medical crowdfunding, particularly regarding donor engagement, contributions per donor, and total campaign funds. The study analyzes data from 3,415 distinct medical campaigns, leveraging an unannounced change on GoFundMe. The findings reveal a complex scenario that defies simple expectations. The leaderboard is expected to increase donor engagement, but it appears to discourage larger contributions per donor, a phenomenon termed “crowding out.” Despite these mixed outcomes, there is a positive correlation between the leaderboard’s presence and the funds raised. These findings highlight the potential of leaderboards to increase engagement and total funds raised in medical crowdfunding campaigns. However, the crowding-out effect raises concerns about the Pareto efficiency of this motivation system. This research contributes insights to both practitioners and theorists, shedding light on the complex interplay between gamification features and crowdfunding outcomes in the medical context.
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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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".