Network tie structure causing OSS group innovation and growth
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) development as an inexpensive process to develop software threatens proprietary software business strategies. Providing business strategy to benefit from volunteer developers for the purpose of contributing to existing projects, as well as initiating new OSS projects is of utmost significance for companies in that industry. Therefore, it is important to figure out how groups of volunteer developers are formed as new developers join existing projects, and it is even more important to investigate what causes these developers to initiate new projects. The authors investigate network structure as a causal factor for both new project initiation within a group (representing group innovation) as well as new developers joining existing projects within a group (representing group growth). The authors develop four hypotheses:1. Intra-group coupling has a positive impact on group growth,2. Inter-group coupling has a positive impact on group innovation,3. Inter-group structural hole has a positive impact on group innovation,4. There is a trade-off between the effects of inter-group structural hole and inter-group coupling on group innovation.The authors test these four hypotheses using data from OSS. Developers contributing to project tasks in groups other than their own can explore novel ideas for new project creation, because they can benefit from sharing knowledge, whereas developers contributing to project tasks inside their own group exploit ideas to improve those existing projects with better inside-group search possibility; and this demands more developers to join those group projects.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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