Bibliographic record
Abstract
Open source software (OSS) development has continued to appear as a puzzling and enigmatic phenomenon and has drawn increasing attention as its importance has grown. Relying upon an alternative way to develop and to distribute software, open source communities have been able to challenge and often outperform proprietary software by enabling better reliability, lower costs, shorter development times, and a higher quality of code (Raymond, 2004). Behind the software is a mass of people working together in loose coordination, even portrayed as a rowdy marketplace (Raymond, 2001, p. 1): No quiet, reverent cathedral-building here—rather, the Linux community seemed to resemble a great babbling bazaar of differing agendas and approaches … out of which a coherent and stable system seemingly emerges only by a succession of miracles. More precisely, the people behind open source projects have been defined as: “Internet-based communities of software developers who voluntarily collaborate in order to develop software that they or their organizations need” (von Krogh, 2003, p. 14). In contrast to the sacred cathedral-like software development model that gave birth to most commercial and proprietary systems, such bazaar-like communities seem to have based their success on a pseudo-anarchic type of collaboration and developers’ interaction (Raymond, 2001). However, in spite of the apparent disorganization of these bazaars, a closer look distinguishes common values and norms that rule them, specific roles that can be identified, similar motives shared by people, and practices that follow patterns. This article highlights key aspects of what forms the communities that support these 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 | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.062 | 0.044 |
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".