On the Relationship Between the Developer's Perceptible Race and\n Ethnicity and the Evaluation of Contributions in OSS
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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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 | 0.012 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".