Legitimacy and Reciprocal Altruism in Donation-Based Crowdfunding: Evidence from India
Bibliographic record
Abstract
The donation-based crowdfunding platforms witness a mix of different entities seeking funding for numerous campaigns, adding complexities in understanding the donor behavior and factors that motivate donation. This study builds upon the economic theory of charitable giving and examines the ethical dilemma that donors face during the selection process. Using the data from Ketto.org, the biggest crowdfunding platform in India, this paper investigates the rank-order preference of donors while making a selection across heterogeneous entities and campaigns. The results show that campaigns run by non-profit organizations registered with causes that qualify for a tax-deduction receive a higher level of funding. Donors then fund unregistered non-profit organizations, followed by campaigns run by individuals. Demonstrating legitimacy by using subtle cues, like tagging “with tax-benefit,” motivates the donors to provide a higher amount of funding.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.002 |
| 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".