Engagement and Crowding-Out Effects of Leaderboard Gamification on Medical Crowdfunding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| 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 it