Who is not developing open source software? non-users, users, and developers
Bibliographic record
Abstract
The development of knowledge requires investment, which may be made in terms of financial resources or time. Open source software (OSS) has challenged much of the traditional reasoning by suggesting that individuals behave altruistically and contribute to a public good, despite the opportunity to free-ride. The lion’s share of the existing literature on OSS examines communities, that is, those individuals whom are already part of the OSS community. In contrast, this paper starts from users with the requisite skill to use and develop OSS. This group of skilled individuals could potentially invest into the development of OSS knowledge, but they may or may not do so in actuality. This paper, therefore, explores three issues, which have not been extensively explored in the literature, namely, (1) how frequently a group of skilled people use OSS, (2) reasons for differences among users and non-users in terms of use and attitudes, and (3) how frequently, and why, some users contribute to OSS projects (and thereby become developers). In doing so, we consider the opportunity costs of use and development of OSS, which has been largely neglected in the literature. We find that the individuals have a rather pragmatic attitude to firms and that many are active in both firms and OSS community, which raises many questions for future research about the role and influence of firms on the development and diffusion of OSS.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".