Comparing Developer Attraction and Turnover in OSS vs. OSS4SG Projects: An MSR Study
Bibliographic record
Abstract
Open source for social good (OSS4SG) projects are projects whose overarching goal focuses on solving a societal issue and driving positive change. Despite the societal importance of such projects and their alignment with the open-source ideology at its very core, there is a clear lack of understanding of the unique characteristics and operational dynamics of OSS4SG projects. In this work, we use Mining Software Repository (MSR) techniques to empirically investigate the community dynamics and project characteristics of OSS4SG projects compared to OSS projects. We leveraged the GitHub API and GraphQL to mine a dataset of 290 projects (199 OSS4SG, 91 OSS) and answer our main research questions: RQ1: How stable are OSS4SG compared to OSS in terms of newcomers and leavers? RQ2: To what extent do turnover and retention of contributors change over time for OSS vs OSS4SG? Namely, we studied and compared the rates at which contributors joined and left OSS and OSS4SG projects at different stages of life (early, mid, late). We classified projects as attractive (high join rate and low leave rate), unattractive (low join rate and high leave rate), stable (low join rate and low leave rate), and unstable (high join rate and high leave rate). Our results show that the majority of OSS4SG projects (60%) are stable, while the majority of OSS projects (74%) are unstable. Our results also show that OSS4SG retains more of their contributors and has less turnover. Looking at the join and leave rates for projects across different life stages, we found that the difference is significant when all stages of life and considered together. However, the difference is far less significant in the late stage of the project's lifespan, indicating that, as they mature, OSS becomes more similar to OSS4SG in terms of community dynamics.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".