Network Analysis of The Evolution of an Open Source Development Community
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
This research investigated the evolution of an open source development community by undertaking network analysis across time. Several open source communities have been previously studied using network analysis techniques, including Apache, Debian, Drupal, Python, and SourceForge. However, only static snapshots of the network were considered by previous research. In this research, we created a tool that can help researchers and practitioners to find out how the Eclipse development community dynamically evolved over time. The input dataset was collected from the Eclipse Foundation, and then the evolution of the Eclipse development community was visualized and analyzed by using different network analysis techniques. Six network analysis techniques were applied: (i) visualization, (ii) weight filtering, (iii) degree centrality, (iv) eigenvector centrality, (v) betweenness centrality, and (vi) closeness centrality. Results include the benefits of performing multiple techniques in combination, and the analysis of the evolution of an open source development community.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.006 | 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