Is the Crowd Colorblind? Founder race and performance in crowdfunding
Bibliographic record
Abstract
The dearth of minority entrepreneurs has received increasing media attention but few academic analyses. In particular, the funding process creates challenges for either audit or correspondence methods, making it difficult to assess the role, or type, of discrimination influencing resource providers. We use a novel approach that combines analyses of 10,194 crowdfunding projects with an experimental design to identify whether African-American men are discriminated against and whether this reflects statistical, taste-based, or unconscious bias on the part of prospective supporters. We find that African- American men are significantly less likely to receive funding that similar white founders, and that prospective supporters rate identical projects as lower in quality when the believe the founder is an African-American male. We conclude that the reduction in perceived quality does not reflect conscious assumptions of differences in founder ability or disamenity but rather an unconscious assumption that black founders are lower quality. In a final experiment we show that the racial penalty can be removed by providing prospective supporters with additional evidence of quality via third-party endorsements (i.e. awards, evidence of prior support).
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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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".