Ecosystems in GitHub and a Method for Ecosystem Identification Using Reference Coupling
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
Software projects are not developed in isolation. Recent research has shifted to studying software ecosystems, communities of projects that depend on each other and are developed together. However, identifying technical dependencies at the ecosystem level can be challenging. In this paper, we propose a new method, known as reference coupling, for detecting technical dependencies between projects. The method establishes dependencies through user-specified cross-references between projects. We use our method to identify ecosystems in GitHub-hosted projects, and we identify several characteristics of the identified ecosystems. We find that most ecosystems are centered around one project and are interconnected with other ecosystems. The predominant type of ecosystems are those that develop tools to support software development. We also found that the project owners' social behaviour aligns well with the technical dependencies within the ecosystem, but project contributors' social behaviour does not align with these dependencies. We conclude with a discussion on future research that is enabled by our reference coupling method.
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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 | 0.002 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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