On the Relationship Between the Developer's Perceptible Race and\n Ethnicity and the Evaluation of Contributions in OSS
Why this work is in the frame
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Bibliographic record
Abstract
Open Source Software (OSS) projects are typically the result of collective\nefforts performed by developers with different backgrounds. Although the\nquality of developers' contributions should be the only factor influencing the\nevaluation of the contributions to OSS projects, recent studies have shown that\ndiversity issues are correlated with the acceptance or rejection of developers'\ncontributions. This paper assists this emerging state-of-the-art body on\ndiversity research with the first empirical study that analyzes how developers'\nperceptible race and ethnicity relates to the evaluation of the contributions\nin OSS. We performed a large-scale quantitative study of OSS projects in\nGitHub. We extracted the developers' perceptible race and ethnicity from their\nnames in GitHub using the Name-Prism tool and applied regression modeling of\ncontributions (i.e, pull requests) data from GHTorrent and GitHub. We observed\nthat among the developers whose perceptible race and ethnicity was captured by\nthe tool, only 16.56% were perceptible as Non-White developers; contributions\nfrom perceptible White developers have about 6-10% higher odds of being\naccepted when compared to contributions from perceptible Non-White developers;\nand submitters with perceptible non-white races and ethnicities are more likely\nto get their pull requests accepted when the integrator is estimated to be from\ntheir same race and ethnicity rather than when the integrator is estimated to\nbe White. Our initial analysis shows a low number of Non-White developers\nparticipating in OSS. Furthermore, the results from our regression analysis\nlead us to believe that there may exist differences between the evaluation of\nthe contributions from different perceptible races and ethnicities. Thus, our\nfindings reinforce the need for further studies on racial and ethnic diversity\nin software engineering to foster healthier OSS communities.\n
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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