The Interplay between Social Structure and Knowledge Reuse in Open Innovation Communities
Bibliographic record
Abstract
Current research provides little insight into knowledge recombination capacities of open innovation communities and the entanglement of these capacities with social mobility of contributors. Extending the literature on network-level determinants of knowledge diffusion and reuse we examine the interrelation between technical ties and social ties connecting open source projects to find out whether social mobility is significantly related to knowledge reuse in communities that are virtually unaffected by legal, economic and social barriers to reuse. We test our hypotheses using the entire population of 61,834 reusable Ruby projects contributed to RubyGems.Org within a period of ten years from the advent of Ruby language in 2003. Our results suggest that developer coaffiliations among projects are likely to coincide with knowledge reuse in form of function calls between projects. A deeper investigation into the temporal ordering of social and technical ties hints at a bi-directional relationship between the two phenomena, whereby new technical ties are more likely to arise in existence of prior social ties and vice versa. This sets our findings apart from the bulk of previous empirical studies that uniquely emphasize the role of social networks as determinants of knowledge diffusion and reuse. These results, on the other hand, corroborate the more recent accounts that underscore the impact of knowledge exchange on social networks, or describe the relation between social ties and knowledge ties as co-evolutionary.
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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 | 0.007 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".