MODELING DISTRIBUTED COLLABORATION ON GITHUB
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
In this paper, we apply concepts from Distributed Leadership, a theory suggesting that leadership is shared among members of an organization, to frame models of contribution that we uncover in five relatively successful open source software (OSS) projects hosted on GitHub. In this qualitative, comparative case study, we show how these projects make use of GitHub features such as pull requests (PRs). We find that projects in which member PRs are more frequently merged with the codebase experience more sustained participation. We also find that projects with higher success rates among contributors and higher contributor retention tend to have more distributed (non-centralized) practices for reviewing and processing PRs. The relationships between organizational form and GitHub practices are enabled and made visible as a result of GitHub's novel interface. Our results demonstrate specific dimensions along which these projects differ and explicate a framework that warrants testing in future studies of OSS, particularly GitHub.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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