Is the Crowd Colorblind? Founder race and performance in 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
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).
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 | 0.001 | 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 | 0.000 | 0.004 |
| Open science | 0.001 | 0.001 |
| 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