How Long and How Much: What to Expect from Summer of Code Participants?
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
Open Source Software (OSS) communities depend on continu-ally recruiting new contributors. Some communities promote initiatives such as Summers of Code to foster contribution, but little is known about how successful these initiatives are. As a case study, we chose Google Summer of Code (GSoC), which is a three-month internship promoting software development by students in several OSS projects. We quantitatively inves-tigated different aspects of students' contribution, including number of commits, code churn, and contribution date inter-vals. We found that 82% of the studied OSS projects merged at least one commit in codebase. When only newcomers are considered, ~54% of OSS projects merged at least one com-mit. We also found that ~23% of newcomers contributed to GSoC projects before knowing they would be accepted. Addi-tionally, we found that the amount of commits and code of students with experience in the GSoC projects are strongly correlated with how much code they produced and how long they remained during and after GSoC. OSS communities can take advantage of our results to balance the trade-offs in-volved in entering CCEs, to set the communities' expectations about how much contribution they can expect to achieve, and for how long students will probably engage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| 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