Network tie structure causing OSS group innovation and growth
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".