Being a Mentor in Open Source Projects
Bibliographic record
Abstract
Abstract A well-known way to help newcomers overcome initial contribution challenges is mentoring. This strategy has proven effective in offline and online communities, and to some extent has been employed in Open Source Software (OSS) projects. Through mentoring, newcomers are trained to acquire the technical, social, and organizational skills they need. Despite the importance of OSS mentors, they are under studied in the literature. Understanding who mentors in OSS projects are, the challenges they face, and the strategies they use can help OSS projects better understand and support mentors' work. In this paper, we investigate the OSS mentors' perspectives by employing a two-stage study. First, we understand the characteristics of the mentors in a large OSS community through a large-scale online survey in the Apache Software Foundation. We found that contributors who are volunteers and less experienced are less likely to take on the role of mentoring. Second, we identify the challenges that mentors face and how they mitigate these challenges through interviews with OSS mentors (n=18). In total, we identified 25 general mentorship challenges and 7 sub-categories of challenges regarding task recommendation. We also identified 13 strategies to overcome these challenges. Our results provide insights for OSS communities, formal mentorship programs like Outreachy, and tool builders who design automated support for task assignment and internship.
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How this classification was reachedexpand
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 | 0.014 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".